Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 88 appraisals
Indian journal of ophthalmology
Internet of things in assistive technology for people with visual impairment: A scoping review
Assistive technology (AT) can significantly enhance functional abilities among persons with disabilities. Recently, a rapid advance in computer and Internet of Things (IoT)-based AT has been observed with the integration of Artificial Intelligence. The present scoping review explores a range of IoT-based AT for vision impairments, focusing on their functioning, reliability, and identifying the existing gaps and opportunities for future enhancement. We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The search was conducted for the articles published between 2020 and March 2025. After the initial screening of both titles and abstracts of 705 articles, 18 full-text articles were selected for the review. It was found that the majority of the papers (38%) are related to smart canes/sticks. The findings indicate that several studies use Raspberry Pi and Arduino platform to make products cost-effective and flexible options for real-time processing. The accuracy of these devices ranges from 87.8% in navigation to 93% in voice control and 100% in object identification. Further, challenges such as usability testing, hardware limitations, dataset constraints, and environmental adaptability remain a matter of concern which need to be addressed in future innovation. Incorporating IoT technologies into vision-assistive devices has substantially improved accessibility. Future research should focus on enhancing real-time performance, AI-driven decision-making, and user-centric designs.
1 Aug 2026
Read appraisal →BMJ open ophthalmology
Screening for diabetic retinopathy with artificial intelligence in a primary care setting: a comparative cost analysis
OBJECTIVE: Cost analysis of autonomous artificial intelligence (AI)-based screening of diabetic retinopathy (DR) for adults with diabetes at a primary care clinic. METHODS AND ANALYSIS: This study provides a comparative cost analysis of actual results using AI-based DR screening with counterfactual results based on all patients going through the physician-based referral system. A cost analysis is conducted using cost data from published sources, provincial billing codes, statistical sources, and patient characteristics from a clinical study to compare autonomous AI-based screening for DR versus physician-based screening. Costs considered include direct costs of operating the AI system, physician fees, and indirect costs to patient time. Along with total cost comparisons, a cost per DR case detected is estimated and a sensitivity analysis based on variations in AI costs is provided. RESULTS: Over the study period, 202 participants were screened for DR using autonomous AI. The majority (93.6%, n=189) of AI-based DR screening exams were completed successfully. The AI-based scenario results in total direct costs of $C7919.04 and indirect costs of $C5728.80, resulting in total costs of $C13 647.84 per 100 patients. The traditional physician-based approach results in total direct costs of $C8240 and indirect costs of $C19 998.09, resulting in total costs of $C28 238.09 for 100 patients. When costs are converted to costs per unit outcome, the total cost per diagnosed DR case is $C620.36 for the AI-based approach and $C1283.55 for the physician-based approach; the AI-based cost per diagnosed case was 52% lower. CONCLUSION: Given the lower cost per diagnosed case of the AI-based approach, there are advantages to the implementation of AI-based screening for DR.
1 Aug 2026
Read appraisal →Science advances
Multispectral infrared-to-full-color upconversion expanding human vision
The human visual system is inherently blind to infrared radiation due to the insufficient energy of infrared photons to trigger the photoisomerization of the retinal chromophore, resulting in the loss of over half of the solar spectrum. Here, we report a colloidal quantum dot (CQD)-based infrared-to-visible upconverter that enables ultrasensitive upconversion of multispectral infrared light radiation as full-color visible vision. The photon-energy-selective excitonic transitions in quantum-confined states and the photon flux, respectively, enable infrared wavelength- and intensity-dependent number of photogenerated carriers. A dual-emissive-layer organic architecture with the strategically engineered hole-injection barrier could route these carriers into distinct color emission channels, resulting in correlated wavelength/intensity-to-color/luminance mapping. This paradigm shift yields a discrimination sensitivity for subtle infrared variations exceeding more than two orders of magnitude higher than conventional single-color modes, capitalizing on the human eye's intrinsic superiority in chromatic differentiation over only brightness contrast. The resulting upconverter exhibits broadband detection extending beyond 2 μm, luminance exceeding 700 cd m-2, and a photon-to-photon conversion efficiency of 3.85%. The upconverter could be applied as a lightweight, semi-transparent wearable eyeglass that projects multispectral infrared as full-color vision directly onto the retina. Besides, upconverters bound to light-sensitive proteins, potentially as an implantable retinal photoreceptor, confer innate infrared vision. By surpassing the evolutionary constraints of natural vision, this work establishes a versatile foundation for next-generation visual prosthetics and human-integrated sensory expansion.
1 Aug 2026
Read appraisal →BMC ophthalmology
The effect of lithium on the structure and function of the human retina: a systematic review
BACKGROUND: Bipolar disorder and depression are associated with structural and functional changes in the retina, including a thinner retinal nerve fibre layer (RNFL). Lithium is widely considered the most effective treatment for bipolar disorder, but its mechanism of action is not fully understood. We assessed research looking at the effect of lithium on structural or functional retinal outcomes in humans. METHODS: Searches using the terms 'Lithium' AND 'retina' were carried out to identify peer reviewed studies assessing the impact of lithium on retinal structure or function. These included those with or without a control group comparison, pre- and post- lithium comparisons and observational studies. There were no exclusions based on the quantity or preparation of lithium administered, or the length of administration. Risk of bias was assessed using the Joanna Briggs Institute (JBI) critical appraisal tool for Analytical Cross Sectional Studies, and a narrative synthesis and tabulated summary of the included studies was completed. RESULTS: Seven studies assessing structural outcomes and 10 reporting functional ones were identified, all highly heterogenous and with multiple limitations. Structural outcomes were derived exclusively from optical coherence tomography (OCT) with retinal nerve fibre layer (RNFL) being the most common measurement. There was no evidence of differences in the RNFL between participants with bipolar disorder taking lithium and healthy controls in two larger studies. In six studies looking at differences in those with bipolar disorder taking lithium and those taking valproate, two showed no signs of difference and four showed evidence of thicker RNFL in the lithium group. Studies reporting functional outcomes reported a statistically significant effect of lithium on at least one functional measure, derived from electrooculography, electroretinography, and dark adaptation thresholds. CONCLUSIONS: Current evidence suggests that lithium is likely to have an effect on the retina but limitations in all studies mean better designed and adequately powered prospective studies are required. REGISTRATION: PROSPERO database (Number-CRD42024516635).
31 July 2026
Read appraisal →PloS one
Artificial intelligence for early detection of diabetic retinopathy: A vision transformer-based approach
BACKGROUND: Early identification of diabetic retinopathy (DR), which is a primary cause of vision impairment globally, is a crucial phasis for effective intervention and treatment. Traditional screening workflows rely on manual diagnosis by ophthalmologists, which remains the gold standard but can be time-consuming and subject to variability due to human factors. To support and enhance the screening process, artificial intelligence (AI)-based tools have shown promise in automating DR detection, particularly with recent advances in deep learning. However, medical images with long-range dependencies and spatial linkages can be challenging for CNN-based algorithms to handle. METHODS: This paper proposes a Vision Transformer (ViT)-based model, specifically using a Compact Convolutional Transformer (CCT), for early automated detection of DR. The model uses self-attention techniques to improve feature extraction and classification performance; combining three main stages: the CCT tokenizer, transformer encoder, and sequence pooling. The proposed approach was trained on public datasets (EyePACS and APTOS 2019) and evaluated against state-of-the-art deep learning architectures. RESULTS: Our experimental findings demonstrate that ViT performs among the best in the current state of the art with an overall accuracy of 97% and F1-scores above 0.95 across all DR severity levels. Our system is primarily designed for the pre-screening stage of diabetic retinopathy workflows, enabling rapid and reliable identification of potential DR cases for further clinical evaluation. CONCLUSION: These results highlight the potential of transformer-based designs in medical picture analysis, as well as the implications for telemedicine and e-health solutions in real-time, especially in cases of low-resource settings.
28 July 2026
Read appraisal →Medicine
A bibliometric analysis of global trends in AI-driven digital health technologies for diabetes management
BACKGROUND: Digital health technologies are increasingly applied in diabetes care, enabling continuous monitoring, personalized support and remote interventions. Meanwhile, artificial intelligence (AI) is enhancing the precision and effectiveness of these tools. This study aims to map global research trends and thematic developments in AI-driven digital health technologies for diabetes management and to explore their future directions. METHODS: We collected data from the Web of Science Core Collection, including articles and reviews published up to July 12, 2025, using CiteSpace, VOSviewer, and Microsoft Excel to analyze countries/regions, institutions, journals, references, authors, and keywords. RESULTS: A total of 673 publications were included in the analysis. Global publications on AI-driven digital health technologies for diabetes increased steadily, with the USA leading in output. The University of London ranked as the most productive institution. Sensors and diabetes care were the most frequently published and cited journals in this field. Herrero P was among the most prolific authors. The most cited article was "Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs." "diabetes" was the most frequently occurring keyword. Keyword cluster analysis identified 3 primary research hotspots: AI-enabled monitoring, digital health interventions, and AI-based diabetic retinopathy screening. CONCLUSIONS: This study summarizes the evolution of AI-driven digital health technologies in diabetes care. Although challenges remain in data security, standardization and validation, these technologies hold increasing potential for accurate diagnosis, real-time monitoring and personalized care.
26 July 2026
Read appraisal →PloS one
Fundus image analysis of retinitis pigmentosa using artificial intelligence
Retinitis pigmentosa (RP) is a group of inherited retinal diseases that are caused by genetic defects that lead to progressive photoreceptor loss and eventual blindness. Early diagnosis would be helpful for effective management of the disease; however, many patients stay unaware of early symptoms. Meanwhile, fundus images are widely obtained during routine medical checkups but are underused for detecting RP. This study explores the effectiveness of finetuning deep learning models, pre-trained for general visual tasks, to identify RP from color fundus images. The dataset comprised 321 color fundus images from 201 Japanese subjects at Keio University Hospital, including 200 images from 107 patients with retinitis pigmentosa and 121 images from 94 non-retinitis pigmentosa subjects. Multiple images were available for some subjects. Using transfer learning, pretrained convolutional neural network models -VGG16, Resnet50, and InceptionV3- were finetuned to detect RP. As a result, Inception V3 achieved the best accuracy of 96.97%, which matches the average diagnostic accuracy of ophthalmologists. Gradient-weighted Class Activation Mapping (Grad-CAM) suggested that the model attended to clinically relevant fundus regions, including the peripheral retina and posterior pole, which may reflect features such as peripheral degenerative changes and retinal vascular attenuation. These findings support the potential interpretability of the finetuned model and suggest that deep learning may assist ophthalmologists in RP screening as a supportive tool.
25 July 2026
Read appraisal →Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation
Quality of life with anophthalmia: a systematic review.
PURPOSE: Anophthalmia due to enucleation, evisceration, or exenteration, results in permanent vision loss that may have profound functional, psychological, and social consequences. The purpose of this study is to evaluate health-related quality of life, vision-related quality of life, and psychosocial outcomes among adults with anophthalmia. METHODS: This systematic review was conducted in accordance with PRISMA 2020 guidelines and prospectively registered on PROSPERO (ID: CRD420251274476). MEDLINE, Embase, and CENTRAL were searched from inception to September 9, 2025. Eligible studies included participants with congenital or acquired anophthalmia and reported outcomes related to quality of life or psychosocial health. Risk of bias was assessed using the Cochrane RoB-2 tool for randomized trials and Joanna Briggs Institute critical appraisal tools for observational studies and results were synthesized narratively. RESULTS: Forty-two studies (1999-2025) including 4544 patients (56.0% male; mean age 49.9 years) were included. Enucleation was the most common procedure (55.1%), and most cases were unilateral (59.6%). Health-related quality of life was commonly assessed using the SF-36 and vision-related quality of life using the NEI VFQ-25. Early postoperative reductions in health-related quality of life were frequently observed, with partial recovery over time. Vision-related quality of life remained persistently impaired, particularly for depth perception, peripheral vision, and driving. While mean anxiety and depression scores were often within normal ranges, a substantial subset of patients experienced ongoing psychological distress and appearance-related concerns. CONCLUSIONS: Acquired anophthalmia is associated with enduring vision-specific and psychosocial challenges, supporting the need for anticipatory counseling and integrated multidisciplinary rehabilitation in clinical practice.
25 July 2026
Read appraisal →PloS one
AI-based color vision screening and educational counseling for high school students: An experimental study
Color vision deficiency (CVD) can negatively affect students' learning experiences and career choices; however, combined screening and educational counseling remain challenging in school settings, particularly in low-resource contexts. Conventional paper-based color vision tests are time-consuming, difficult to standardize, and rarely linked to individualized educational or career guidance. This study aimed to design, implement, and validate an Android-based mobile application that integrates CVD screening with artificial intelligence (AI)-assisted educational and career counseling for high school students. A cross-sectional study was conducted with 527 high school students in Danang, Viet Nam. The application incorporated a digital Ishihara-based screening module and an AI-assisted counseling component. Screening outcomes were compared with conventional Ishihara testing to evaluate agreement, while students' perceptions of usability, usefulness, and counseling relevance were assessed using a validated questionnaire. Statistical analyses included descriptive statistics, independent-samples t-tests, reliability analysis using Cronbach's alpha, and exploratory factor analysis. Seventeen students (3.23%) were identified as having CVD, including red-green, protan, deutan, and achromatopsia (complete CVD). The mobile application demonstrated high concordance with conventional screening results. No statistically significant differences were observed between students with and without CVD in overall system evaluation scores (p > 0.05). The questionnaire showed good internal consistency (Cronbach's alpha = 0.857) and a clear multi-factor structure reflecting students' acceptance of AI-assisted educational and career counseling. These findings indicate that the proposed mHealth application offers a feasible and scalable approach for school-based CVD screening combined with personalized educational and career guidance. The system may serve as a supportive decision-making tool for students and counselors, while further validation under diverse real-world conditions and across different mobile devices is recommended.
24 July 2026
Read appraisal →International ophthalmology
Machine learning-based model for identifying liver injury in patients with thyroid-associated ophthalmopathy
PURPOSE: To explore the relationship between thyroid-associated ophthalmopathy (TAO) and liver injury and establish a model for identifying liver injury by using machine learning so as to provide an effective diagnostic tool for liver injury in patients with TAO. METHOD: A single-center retrospective study was conducted to collect the clinical data of 318 patients with TAO admitted to a hospital from 2016 to 2022. The patients were divided into a TAO liver injury group (104 cases) and TAO normal liver function group (214 cases) according to whether the patients had normal liver function. The multivariate binomial logistic regression model was used to analyse the risk factors for liver injury in patients with TAO. Feature selection was performed using the random forest algorithm. The research data were divided into a training set and a test set at a ratio of 6:4. Taking whether accompanied by liver injury (0 = no, 1 = yes) as the outcome variable, models were established based on logistic regression, random forest, support vector machine, and decision tree. The performance of the models was evaluated using metrics, including sensitivity, specificity, positive predictive value, negative predictive value, Youden index, and accuracy. RESULT: The random forest method was used for feature screening, and variables ranked among the top 10 according to either mean decrease accuracy or mean decrease Gini were selected for model construction. In five-fold cross-validation, the RF model showed the highest accuracy of 0.937 and AUC of 0.977. In the test set, the RF model also showed good discrimination, with an AUC of 0.973, and the SVM model showed the highest AUC of 0.986, while SVM and LR achieved the highest accuracy of 0.914. CONCLUSION: This study shows that a classification model based on machine learning can effectively identify the risk of liver damage in patients with TAO.
18 July 2026
Read appraisal →International ophthalmology
Safety and efficacy of silicone intubation compared to standard techniques in endoscopic dacryocystorhinostomy: a systematic review and meta-analysis
BACKGROUND: Primary Acquired nasolacrimal duct obstruction (PANDO) is a lacrimal drainage disorder, commonly seen in adults. Endoscopic Dacryocystorhinostomy (EN-DCR) is a widely adopted surgical procedure for treatment of nasolacrimal duct obstruction. Silicon intubation is a technique, which involves inserting a soft silicone stent to keep the ostium patent post operatively. METHODS: This meta-analysis followed Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) 2020 guidelines, with searches across 4 databases. Primary outcomes were overall success rate, and overall failure rate. Secondary outcomes included granulations/intracanalicular obstruction (ICO) threats, hemorrhage, anatomical failure, functional failure, recurrent symptoms/epiphora, and cicatricial stenosis/post-procedural restenosis. Data were analyzed using R software version 4.3. Heterogeneity was assessed using the I2 statistic. RESULTS: From 3,974 studies initially, 20 studies were selected in the final review, in which 17 were RCTs and 3 observational studies. A statistically significant improvement in surgical success rates, while reducing failure rates were observed in the silicone intubation (stented) group compared to the unstented group. No significant difference was observed in the incidence of restenosis, hemorrhage, functional failure, epiphora or recurrent symptoms. Reduction of anatomical failure was observed. Increase in adverse effects of granulation tissue formation and internal canalicular obstruction were observed. Heterogeneity was low and no publication bias was detected in outcomes. CONCLUSIONS: Silicone intubation during mechanical endoscopic dacryocystorhinostomy (EN-DCR) was associated with improved success and patency rates, as well as lower failure rate. While more resource-intensive, it provides a slightly higher advantage, particularly in recurrent or revision instances, recommending its implementation in routine EN-DCR where possible.
16 July 2026
Read appraisal →International ophthalmology
The relationship between diabetic retinopathy and intestinal microbiota: a systematic review and meta-analysis
PURPOSE: The results of human observational studies on the correlation between gut microbiota and diabetic retinopathy (DR) are discrepant. This meta-analysis aimed to evaluate the specificity of the gut microbiota in DR patients compared to patients with type 2 diabetes mellitus (T2DM). METHODS: All published literature up to October 2024 was searched by two researchers on PubMed, Embase and Web of Science databases. Diversity and gut microbiota composition were the main outcomes. The meta-analysis was conducted in Review Manager (RevMan) Version 5.3. RESULTS: Eight studies, investigating gut microbiota by collecting stool samples, conducted in China and India were included in this meta-analysis, involving a total of 486 individuals in the T2DM (n = 258) and DR (n = 228) groups. No significant difference in alpha-diversity was observed between T2DM and DR patients. The pooled estimate showed that, at the phylum level, the abundances of Patescibacteria, and Synergistetes were significantly lower, and Verrucomicrobia were considerably higher in DR patients than in T2DM patients. At the genus level, DR patients had an increase in Bacteroides compared to T2DM. CONCLUSIONS: In this meta-analysis with a small number of studies and relatively high heterogeneity, changes in gut microbiota were associated with DR, commonly reflected by a reduction in beneficial species and an increase in pathogenic species influencing metabolic pathways.
10 July 2026
Read appraisal →Science advances
Toward autonomous robotic-assisted and microrobotic surgery
Autonomous robotic-assisted surgery (RAS) has emerged as a promising objective in biomedical technology, further enhanced by miniaturization toward microrobotic-assisted surgery (μ-RAS). This reduction in scale promises minimally invasive, partially or fully automated surgical procedures, with the potential to reduce patient recovery times, lower medical costs, and enable previously unavailable procedural options. This perspective highlights the specific advances in RAS that potentially map to the microscale (μ-RAS), organized across five surgical domains: endovascular, endoluminal, laparoscopic, ophthalmic, and orthopedic. We examine both clinical demands and technological advances in surgical robotics and identify the key innovations required for progress across these surgical fields. Our contribution is distinct in combining the perspectives of both surgical experts and bioengineering innovators, outlining a roadmap for the advancement and eventual integration of autonomous RAS and μ-RAS into mainstream surgical practice.
6 July 2026
Read appraisal →Medical image analysis
STAGE challenge: Structural-Functional Transition in Glaucoma Assessment
Glaucoma is a leading cause of irreversible yet preventable blindness in working-age populations. Clinical diagnos is currently relies on functional visual field (VF) examinations to evaluate visual function and monitor disease progression, but these tests are time-consuming and require close cooperation with ophthalmologists, limiting their practicality. Although deep learning has shown promise for glaucoma diagnosis, most models have focused on structural changes in fundus and OCT images without linking them to functional VF outcomes. To bridge this gap, the HDMI Laboratory, in collaboration with the Zhongshan Ophthalmic Center of Sun Yat-sen University, organized the STAGE Challenge: Structural-Functional Transition in Glaucoma Assessment. The challenge explores predicting functional VF indicators-mean deviation, sensitivity maps, and pattern deviation probability maps-directly from structural OCT images. A dataset of 401 OCT volumes (each with 256 cross-sectional images) is released with corresponding VF labels and demographic data, along with a standardized evaluation framework to ensure fair comparison. This paper summarizes the methods of the seven finalist teams and analyzes their results. All teams employed dual-branch architectures integrating OCT with tabular data, and those using task-specific OCT models achieved the highest performance. These findings highlight the importance of tailored deep learning strategies for linking structural imaging to functional outcomes. The STAGE Challenge thus establishes the first standardized benchmark with a large, curated dataset for this task, enabling systematic evaluation of algorithms for structure-function analysis in glaucoma, and providing a foundation for future research. Details of the competition are available at http://hdmilab.cn/competition/stage.
3 July 2026
Read appraisal →Experimental eye research
Artificial intelligence applications in OCT and OCTA for diabetic retinopathy: A systematic review
PURPOSE: Diabetic retinopathy (DR) is a leading cause of vision impairment worldwide. Optical coherence tomography (OCT) and OCT angiography (OCTA) provide detailed retinal imaging, enabling early detection of microvascular changes. This study aims to systematically review artificial intelligence (AI), particularly deep learning (DL), applications for DR detection and analysis using OCT and OCTA images. METHODS: A comprehensive literature search was conducted across PubMed, Web of Science, Scopus, IEEE Xplore, and Embase for studies published up to March 2026. A total of 1007 articles were identified, of which 175 studies met the inclusion criteria following the PRISMA study selection process. RESULTS: DL-based approaches consistently demonstrated superior performance compared to traditional machine learning (ML) methods, with reported AUC values typically ranging from 0.90 to 0.99 across classification and segmentation tasks. Convolutional neural networks (CNNs), Vision Transformers (ViTs), and encoder-decoder architectures such as U-Net showed strong performance in detecting key DR biomarkers, including microaneurysms, macular edema, and neovascularization. However, performance variability was observed depending on dataset size, imaging modality, and annotation quality. CONCLUSIONS: AI-driven analysis of OCT and OCTA images offers significant potential for automated DR detection. Despite promising results, challenges such as limited public datasets, lack of cross-institutional validation, and model interpretability remain. Future research should focus on multimodal integration, explainable AI, and large-scale validation to enhance clinical applicability.
2 July 2026
Read appraisal →Journal of neuro-oncology
Endovascular treatment of tumor-related dural sinus stenosis, venous outflow obstruction, and secondary intracranial hypertension: a systematic review of the literature.
INTRODUCTION: Tumors involving dural venous sinuses can lead to venous outflow obstruction and secondary intracranial hypertension. While surgical resection can decompress the sinus, such procedures carry significant risks. With the success of endovascular interventions for idiopathic intracranial hypertension (IIH), venous sinus stenting (VSS) and angioplasty have emerged as potential treatments for tumor-related sinus stenosis. However, evidence remains limited and dispersed. This systematic review aimed to synthesize the available literature on the safety and efficacy of endovascular treatment for tumor-related venous sinus obstruction. METHODS: A systematic search of PubMed, Embase, and Web of Science was conducted from database inception to November 2025. Eligible studies included any report describing endovascular treatment (VSS and/or angioplasty) for tumor-related venous sinus obstruction, whether due to tumor invasion or compression, or even stenosis following surgery or radiotherapy. Data on patient demographics, tumor characteristics, procedural details, and outcomes were extracted and summarized descriptively. RESULTS: Twenty studies comprising 57 patients were included. Most patients were female (65%) with a mean age of 48 years. Meningioma represented the most common underlying tumor (75%). The majority of obstructions resulted from tumor compression or invasion (86%), and most patients underwent VSS alone (74%), while the rest underwent VSS and angioplasty (23%). No periprocedural complications were reported. Mean trans-stenotic pressure gradient decreased by 10 ± 9 mmHg post-treatment and mean lumbar opening pressure decreased by 11 ± 12 cmH2O. Clinical improvement was reported in 63% of patients, including resolution or improvement of papilledema in 94% and vision changes in 94%. Repeat intervention was required in 28% of cases, and only about half of patients achieved further improvement after reintervention. CONCLUSION: Endovascular therapy for tumor-related venous sinus stenosis appears safe and effective, leading to meaningful pressure reduction and symptomatic improvement in two-thirds of patients. These findings support VSS as a viable adjunct or alternative to surgery in selected patients. Nonetheless, the certainty of evidence remains very low, and current data cannot establish definitive conclusions.
28 June 2026
Read appraisal →Science advances
Smart contact lens-trained digital twin for device-free personalized uric acid prediction
Tears contain valuable biomarkers and offer potential for noninvasive disease monitoring. However, the lack of correlation analysis between serum uric acid (SUA) and tear uric acid (TUA) has limited their clinical application in personalized medicine. Here, we present a wireless smart contact lens capable of real-time, noninvasive monitoring of TUA as an alternative to blood-based UA testing. We validated the device through correlation analysis in rabbits and human participants, including individuals with hyperuricemia and gout. Daily-life monitoring enabled personalized characterization of TUA fluctuations in response to food intake and physical activity, together with individualized lag time profiling. A strong linear relationship allowed development of a regression model to derive estimated SUA from TUA. Building on these temporal profiles, lifestyle-informed digital twins were constructed to predict daily uric acid dynamics without continuous lens wear. This digital twin-enabled, device-free framework provides a practical route toward unobtrusive and personalized metabolic health monitoring.
27 June 2026
Read appraisal →International ophthalmology clinics
Beyond the Clinic: Home-Based Monitoring Strategies in Glaucoma Care
Glaucoma is a chronic and progressive optic neuropathy, necessitating lifelong monitoring for affected individuals. Traditional surveillance of the disease relies on evaluation of intraocular pressure (IOP), optic nerve anatomy, and visual function at the physician's office, which only provides limited snapshots of a disease characterized by dynamic physiological variation. Developments in portable ophthalmological technologies and digital health platforms have enabled home monitoring of glaucoma. This review synthesizes current evidence on the home monitoring of glaucoma, with a focus on self-tonometry and home-based visual field testing. Handheld tonometers such as iCare HOME and iCare HOME2 allow repeated, anesthesia-free self-measurement of IOP in a real-world setting. These devices have demonstrated good agreement with Goldmann applanation tonometry, the gold standard. Home tonometry captures diurnal variations of IOP, thus providing relevant insights for diagnosis and therapeutic decision-making. Home-based visual field testing (eg, tablet-based platforms, virtual reality-based testing) enables frequent, unsupervised functional assessment with repeatability and sensitivity comparable to standard automated perimetry. Increased testing frequency may facilitate earlier detection of rapid progression, particularly in the early stages of disease. Collectively, evidence suggests the feasibility and clinical relevance of these home monitoring modalities. However, patient compliance with testing schedules and cost-effectiveness remain as challenges.
24 June 2026
Read appraisal →European journal of trauma and emergency surgery : official publication of the European Trauma Society
Pattern of blast injuries. A systematic review: Part 2 - Landmines, unexploded ordnance and terrorism.
PURPOSE: This systematic review analyzes the patterns and distribution of blast injuries from landmine explosions, UXOs and terror attacks including suicide bombing, in civilians and military personnel. METHODS: A search was conducted in two databases on 26. March 2025 for observational studies reporting blast injuries in civilian and military populations of all ages. Studies were clustered by explosion typ. A narrative data synthesis was performed. The JBI tool for case series was used. PROSPERO: CRD42023391736. RESULTS: 126 studies were included in the overall systematic review. 45 studies on terror & suicide bombings and 12 studies on landmines & UXOs are analyzed in the present paper. Studies on landmines from 1980 to 2006 included 13,270 individuals (age: 24.0 years (Range 8.2 years - 32.2 years); mainly male (88.9%)) in post-conflict low- and middle-income countries. Lower extremity injuries including amputations were most frequent, followed by eye injuries. Studies on terrorist attacks with a total of 12,518 mainly civilian victims (average age: 33,1 years; 61.6% males) reported on incidents in post-conflict regions and high-income cities mainly from the early 2000s. The injury pattern was multidimensional, with extremity injuries most frequent (28%-30%), including fractures, lacerations, and traumatic amputations (18%-20%). CONCLUSION: This Systematic Review Part 2 on landmines and terror attacks found that extremity injuries, including traumatic amputations, are the most frequent and dominate injury. However, the injury pattern varies depending on the explosion mechanism. Landmines primarily cause lower extremity injuries, as well as eye injuries. Terror and suicide explosions exhibit multidimensional injury pattern depending on the distance and location of the explosion.
19 June 2026
Read appraisal →International journal of medical informatics
Accuracy and completeness of large language models in Epidemic keratoconjunctivitis Queries: A Comparative study
BACKGROUND: Large language models (LLMs) are increasingly applied in clinical contexts, yet their reliability in disease-specific ophthalmic domains remains insufficiently characterized. Epidemic keratoconjunctivitis (EKC), a highly contagious adenoviral ocular infection, represents a critical area where accurate diagnostic and therapeutic information is essential. This study aimed to assess and compare the accuracy and completeness of responses generated by two widely used LLMs, namely ChatGPT and Meta AI, to standardized ophthalmology questions related to EKC. METHODS: This cross-sectional expert evaluation study was conducted between January and March 2025. Thirty-four structured questions covering five EKC domains (etiology, clinical features, diagnosis, treatment, and prognosis) were developed using authoritative ophthalmology sources. Responses generated by ChatGPT (Plus subscription, web interface) and Meta AI (web interface) were independently scored by two fellowship-trained cornea specialists using a five-point Likert scale for accuracy and completeness. Inter-rater agreement was assessed using Cohen's κ and mean absolute error (MAE). RESULTS: Both models demonstrated strong overall performance, with mean accuracy and completeness scores of 4.87 and 4.90 for ChatGPT and 4.87 and 4.93 for Meta AI, respectively. Agreement between expert raters was substantial (κ range 0.78-0.88). Domain-specific analysis revealed consistently high scores across all categories, with Meta AI showing slightly higher completeness, particularly in diagnostic and symptom-related responses. CONCLUSION: LLMs exhibit consistently strong performance and domain consistency when addressing ophthalmic queries related to EKC. Their performance supports their potential use as adjunct informational and educational tools for ophthalmic conditions such as EKC, particularly for structured knowledge retrieval, treatment explanation, and patient counseling, provided that use remains under appropriate professional oversight.
15 June 2026
Read appraisal →Scandinavian journal of primary health care
The diagnostic accuracy of AI-assisted diabetic retinopathy screening in primary care: a prospective validation study
OBJECTIVES: This study investigated the diagnostic accuracy of AI-assisted diabetic retinopathy screening in primary care, using ophthalmologist-led screening as the reference standard. METHODS: Patients with type 2 diabetes attending routine appointments at 10 primary care clinics underwent AI-assisted screening, followed by re-screening at an ophthalmology clinic. The quality of fundus images captured in primary care was independently assessed, and diagnostic accuracy was evaluated by comparing AI-assisted results with ophthalmologist results, including sensitivity, specificity, PPV, NPV, and AUC. Two analyses were conducted: one including all images and one excluding those of poor quality. RESULTS: Among 183 patients (336 images), 18.6% of images were classified as poor quality. When all images were included, the AI-assisted screening achieved a sensitivity of 73.7%, specificity of 90.2%, PPV of 31.1%, NPV of 98.3%, and AUC of 0.82. Excluding poor-quality images improved sensitivity to 80.0%, NPV to 98.7%, and AUC to 0.84. Additional ocular findings unrelated to diabetic retinopathy were observed in 96 patients, including confirmed or non-specific signs of glaucoma, cataract, age-macular degeneration, benign nevus and reduced visual acuity. CONCLUSION: AI-assisted screening in primary care shows potential for clinical application, but further validation in larger populations and improvements in image quality are needed before clinical implementation.
14 June 2026
Read appraisal →BMJ (Clinical research ed.)
Low concentration atropine eye drops and progression of myopia in children: multicentre placebo controlled, double masked, randomised trial in the UK (CHAMP-UK)
OBJECTIVES: To evaluate the efficacy and safety of low concentration atropine eye drops for reducing progression of myopia in children in the UK. DESIGN: Multicentre, double masked, superiority, placebo controlled, randomised trial. SETTING: National Health Service hospital eye services and academic institutions at five UK centres. PARTICIPANTS: 289 children aged 6-12 years with myopia between -0.50 and -10.0 dioptres (D). Participants were allocated in ratio of 2:1 to atropine or placebo. INTERVENTIONS: One eye drop of preserved atropine 0.01% or placebo daily for two years. MAIN OUTCOME MEASURES: The primary outcome was spherical equivalent refractive error of both eyes measured by autorefractor under cycloplegia after two years. Secondary outcomes included change in axial length, best corrected distance and near visual acuity, reading speed, pupil diameter, spectacle correction, adverse event rates, quality of life, and tolerability. Outcomes were collected every six months. An electronic monitoring system was used to assess adherence. RESULTS: 192 participants were included in the atropine group and 97 in the placebo group, with an average age of 9.3 years (standard deviation (SD) 1.7 years). 207 (72%) reported white ethnicity, 161 (56%) were girls, and the mean level of myopia was -2.87 D (SD 1.71 D). A total of 235 (81%) participants completed the study, with primary outcome data available for 230 (80%) participants: 151 (79%) in the atropine group and 79 (81%) in the placebo group. Atropine eye drops were more effective than placebo in reducing myopia progression (mean difference 0.33 D, 95% confidence interval (CI) 0.17 to 0.49 D, P<0.001). Prespecified subgroup analyses did not show differences according to age, ethnicity, sex, or severity of myopia. Changes in central axial length were significantly less in the atropine group versus placebo group: mean difference 0.14 mm (95% CI 0.07 to 0.21, P<0.001). There were no differences in other secondary outcomes, except pupil diameter, which was greater in the atropine group (0.36 mm, 95% CI 0.17, 0.55, P<0.001), and no differences in frequency of adverse events or in tolerability measures. No serious adverse events were related to the trial drugs. CONCLUSIONS: Low concentration atropine (0.01%) eye drops significantly reduced progression of myopia and were well tolerated compared with placebo in children in the UK. TRIAL REGISTRATION: ISRCTN registry ISRCTN99883695, ClinicalTrials.gov NCT03690089.
13 June 2026
Read appraisal →PloS one
Long term evaluation of the safety and efficacy of local cooling anesthesia during intravitreal injections: The COOL-2 Trial
BACKGROUND/OBJECTIVE: To evaluate the safety and effectiveness of a medical device providing cooling anesthesia to the eye as local anesthesia for intravitreal (IVT) injections. SUBJECTS/METHODS: As a multicenter, open label, dose escalation clinical trial, subjects receiving at least 3 IVT injections in the study eye were recruited to participate. All patients received subconjunctival lidocaine as their anesthesia for previous IVT injections. Subjects receiving IVT injections were assigned (non-randomized) to four groups in which the cooling device was applied to the conjunctiva at the location of the intravitreal injection: Group 1: -10˚C for 20 seconds; Group 2: -15˚C for 10 seconds; Group 3: -15˚C for 15 seconds; and Group 4: -15˚C for 20 seconds. The primary outcome was pain during IVT injection assessed by a visual analog scale (0-10). RESULTS: Eighty subjects were enrolled at 2 sites. The mean number of prior IVT injections was 20.3 for Group 2 and 15.0 for Group 3. Pain was significantly less for the 10 second vs the 15 second application (2.2 vs 3.6, p = 0.0255). The mean pain score was lower for Group 2 (-15°C for 10 seconds) ranging a score of 1.0 to 2.9 across the study visits, compared with Group 3 (-15°C for 15 seconds) with a mean range of 3.2 to 5.2 across the study visits. Treatment order (visit) significantly decreased by 0.10 pain units per visit (p = 0.0002). Punctate epithelial erosions (PEE) were reported in 20 of 56 eyes (35.7%) in Group 2, none were reported in Group 3. CONCLUSION: Cooling anesthesia device reduced the severity of pain associated with IVT injection during the procedure, although mild and transient PEE may be associated with treatment. TRIAL REGISTRATION: ClinicalTrials.gov identifier, NCT03956797.
13 June 2026
Read appraisal →BMJ open
Diabetic retinopathy treatment cascade and care continuum in the USA: a systematic review
OBJECTIVES: To characterise engagement across the diabetic retinopathy (DR) care continuum in the USA using a cascade-of-care framework and identify gaps contributing to preventable vision loss. DESIGN: Systematic review. DATA SOURCES: From September to November 2025, with an updated search in March to April 2026 using the same eligibility criteria, we systematically searched Ovid MEDLINE and Ovid Embase supplemented by reviewing reference lists of relevant articles and opportunistic searches of the Centers for Disease Control and Prevention publications. ELIGIBILITY CRITERIA: We included English-language US-based cross-sectional, cohort and case-control studies and systematic reviews containing US-based data published between 1 January 2018 and 31 December 2025 relevant to one of the defined DR cascade stages: (1) diagnosis of diabetes, (2) adherence to DR screening, (3) diagnosis of DR, (4) adherence to DR care and (5) DR-related blindness. We included systematic reviews only to inform DR-related blindness, where primary data were limited and excluded them from other stages to avoid double-counting. Exclusion criteria included studies not relevant to one of the defined DR cascade stages and editorial, perspective or commentary pieces. DATA EXTRACTION AND SYNTHESIS: Two reviewers independently screened studies, extracted data and assessed risk-of-bias using the Newcastle-Ottawa Scale and Risk of Bias in Systematic Reviews tool. We synthesised data narratively and organised via the DR treatment cascade framework. RESULTS: Of 14 893 studies screened, 46 met the inclusion criteria. Cascade analysis revealed substantial losses in patient engagement at three stages: (1) only 15.5%-78.7% (median 59.4%, IQR 33.9%-74.0%) of individuals with diabetes obtain biennial DR screening; (2) a substantial 54.9%-88.5% (median 70.1%, IQR 62.5%-79.3%) of individuals with DR are unaware of their diagnosis; (3) only 30.9%-62.7% (median 52.0%, IQR 40.9%-59.1%) of individuals diagnosed with DR are initially linked to care and 55.3%-77.8% (median 70.3%, IQR 59.2%-77.7%) have a lapse in DR follow-up. CONCLUSIONS: This review identifies major gaps in the DR care continuum, particularly in diagnosis awareness, linkage to care and follow-up adherence. The cascade framework highlights key points of disengagement and provides a basis for prioritising future research.
13 June 2026
Read appraisal →The New England journal of medicine
Subretinal Gene Therapy for X-Linked Retinoschisis
BACKGROUND: X-linked retinoschisis is a recessive disease characterized by progressive macular degeneration and vision loss due to pathogenic variation in RS1. METHODS: We administered a single subretinal injection of an AAV8 vector containing human RS1 complementary DNA (scAAV8-hRS1) into one eye of patients 5 to 18 years of age who had X-linked retinoschisis. The primary end point was safety during the 52-week period after injection. Secondary end points included the change from baseline to week 52 in the best corrected visual acuity (BCVA), retinal structure (assessed with swept-source optical coherence tomography; SS-OCT), the function of photoreceptor and bipolar cells (assessed with full-field electroretinography), and macular sensitivity to light (assessed with microperimetry). RESULTS: A total of 12 patients were enrolled. The dose-escalation phase included two cohorts of 3 patients each who received scAAV8-hRS1 at a dose of 7.5×1010 or 1×1011 vector genomes. In the dose-expansion phase, 3 additional patients were enrolled in each cohort. Overall, 56 adverse events were reported during the 52 weeks after surgery. No patient was reported to have an adverse event of grade 3 or higher or ocular inflammation. A macular hole in the treated eye was observed at week 1 in 1 patient. The mean increase at week 52 in the BCVA was 10.8 letters among the treated eyes and 2.4 letters among the untreated eyes. SS-OCT imaging showed closure of the macular schisis cavity by week 13 in the treated eye in all 12 patients. The mean change at week 52 in central retinal thickness was -437.7 μm among the treated eyes and -17.2 μm among the untreated eyes; the outer retinal layers in the treated eyes of 9 patients were continuous at week 52. No clinically meaningful changes in the function of photoreceptor and bipolar cells or macular retinal sensitivity were observed in the treated eyes. CONCLUSIONS: In this study of subretinal gene therapy with scAAV8-hRS1 in 12 patients with X-linked retinoschisis, there were no reports of adverse events of grade 3 or higher or ocular inflammation. Further clinical testing of scAAV8-hRS1 is warranted. (Funded by the National Natural Science Foundation of China and others; Chinese Clinical Trial Registry number, ChiCTR2300076682.).
13 June 2026
Read appraisal →Scientific data
A Dataset of Widefield Fundus Images From Patients With Birdshot Chorioretinitis and Matched Control
Birdshot chorioretinitis is a rare uveitis where research is limited by the lack of large, publicly available imaging datasets. To address this gap, we present Birdshot-Wide, a standardized retinal imaging dataset comprising 5,042 widefield fundus photographs from 742 eyes with birdshot chorioretinitis (BSCR) and 1,310 images from 742 age-and sex- matched healthy eyes controls. The BSCR cohort is longitudinal, with a median follow-up of 4.31 years. Each image in the BSCR cohort was manually reviewed for quality and classified by dominant lesion subtype. To demonstrate its utility, the dataset was benchmarked on a classification task, where deep learning models trained to differentiate BSCR from control images achieved an area under the receiver operating characteristic curve of up to 0.96. This dataset, with its detailed metadata, provides a valuable resource for biomarker discovery, the development of automated diagnostic tools, and disease progression modeling.
8 June 2026
Read appraisal →Scientific reports
Prevalence and Risk Factors for Cytomegalovirus Retinitis Among People Living with HIV in Sub-Saharan Africa in the Antiretroviral Therapy Era: A Systematic Review and Meta-Analysis
Cytomegalovirus retinitis has remained an alarming and potentially vision-threatening opportunistic disease in the population living with HIV, especially in cases of extreme immunosuppression. Although the introduction of antiretroviral therapy has led to a drastic decline in the number of cytomegalovirus retinitis cases reported in most parts of the world, the current epidemiology of cytomegalovirus retinitis in sub-Saharan Africa has seemed unusual in early reports, unexpectedly portraying low disease prevalence rates despite the high number of HIV cases in the concerned region. This systematic review and meta-analysis aimed to estimate the pooled prevalence of cytomegalovirus retinitis among people living with HIV in sub-Saharan Africa in the antiretroviral therapy era, and to identify associated risk factors. The search of databases included Medline using the search engine PubMed, Google Scholar, and the African Journals Online database named AJOL. For inclusion, studies must have included an eye examination to verify the presence of cytomegalovirus retinitis. The data was combined using the logit transformation model. Heterogeneity was tested using the I-squared test and the Cochran Q test. A total of ten studies involving 1,931 participants from nine sub-Saharan Africa countries were found eligible. The proportion of cytomegalovirus retinitis varied from 0.34% to 15.8%. Pooled prevalence was found to be 3% (95% CI: 1.6-5.4%) with low to moderate inter-study variation (I² = 17.8%). Substantial variation in the proportion was found in different sub-Saharan Africa regions; it was found to be less than 1% in West Africa, 3-4% in Southern Africa, 7% in East Africa and 6% in Central Africa. Across all studies, the strongest and most consistent risk factors for cytomegalovirus retinitis were profound immunosuppression, particularly CD4 < 50 cells/µL. Antiretroviral therapy-naïve status, poor adherence, and presence of advanced AIDS-defining illnesses were additional contextual risk markers. Cytomegalovirus retinitis is an under-recognized but important source of preventable blindness among people living with HIV. Prevalence in sub-Saharan Africa differed from that in Asian early antiretroviral therapy era cohorts but is relevant in settings where late HIV presentation occurs. Systematic retinal screening in people living with HIV with low CD4 counts < 50 cells/µL and expanded HIV care and treatment services are important to prevent blindness due to cytomegalovirus retinitis.
31 May 2026
Read appraisal →Journal of global health
Global, regional, and national trends in blindness and vision loss, 1990-2021: a secondary ecological trend analysis based on modelled population estimates.
BACKGROUND: We aimed to provide global, regional, and national estimates of the burden of blindness and vision loss from 1990 to 2021, stratified by cause, age, and sociodemographic index (SDI). METHODS: This population-based study used data from the 2021 Global Burden of Diseases, Injuries, and Risk Factors Study 2021 (GBD) study, covering 1990 to 2021. It evaluates the burden of blindness and vision loss based on prevalence, age-standardised prevalence rate (ASPR), disability-adjusted life years (DALYs), age-standardised DALY rate (ASR of DALY), and average annual percent changes (AAPCs). RESULTS: Globally, the ASPR of blindness and vision loss increased from 12 453.52 (95% uncertainty interval (UI) = 10 287.58, 15 226.09) in 1990 to 15 784.33 (95% UI = 12 761.44, 19 502.32) in 2021, with an AAPC of 0.85 (95% confidence interval (CI) = 0.7, 1.11, P < 0.001). In contrast, the ASR of DALYs remained stable, changing from 342.01 (95% UI = 237.87, 482.17) in 1990 to 342.78 (95% UI = 224.21, 503.61) in 2021, with an AAPC of 0.05 (95% CI = -0.04, 0.2, P = 0.298). Notably, near vision loss and cataract showed consistent increases, with AAPCs of 1.14 (95% CI = 0.94, 1.51, P < 0.001) and 0.25 (95% CI = 0.16, 0.33, P < 0.001), respectively. Blindness and vision loss prevalence was significantly higher in low and low-middle SDI regions compared to other SDI regions. CONCLUSIONS: Global blindness and vision loss increased from 1990 to 2021, while DALYs stayed stable. Prevention should be tailored by region, country, gender, cause, and age.
31 May 2026
Read appraisal →Journal of medical Internet research
Rerouting Eye Care: How AI and Telemedicine Are Reshaping Ophthalmology Patient Journeys in the United Kingdom and Germany
Artificial intelligence (AI) as a medical device is now progressively entering routine ophthalmic care, yet its impact is still mostly evaluated in terms of diagnostic performance rather than how it reshapes patient care pathways. This viewpoint argues that careful pathway design is crucial to implementing AI in ophthalmology so that it translates into real, practical benefits for patients and services. We propose a framework that classifies AI- and telemedicine-enabled eye care pathways by the initial point of contact and the role of AI: from direct human assessment to grader- or telemedicine-based use to AI-first contact and fully autonomous AI gatekeeping. We apply this framework to 2 contrasting health systems, the United Kingdom and Germany, focusing on common retinal diseases and cardiovascular risk assessment from retinal images. In the United Kingdom, AI is being introduced mainly as a modular upgrade to standardized programs layered onto a gatekeeper model centered on community optometrists and general practitioners. In Germany, direct access to office-based ophthalmologists, opportunistic screening, and commercial retail offerings are producing more fragmented clinician- and market-driven AI adoption. Our comparison shows that the same AI technologies can generate very different patient journeys across health systems. Therefore, their value depends not only on diagnostic performance but also on intentional pathway design, including clear escalation rules and structured information transfer. This includes appropriate task shifting from ophthalmologists to trained staff or new roles, such as image grading, tele-ophthalmology triage, or pathway management. Without such design, AI risks duplicate testing, incomplete or poorly coordinated referrals, and further fragmentation of care.
29 May 2026
Read appraisal →PloS one
Development and evaluation of AI model with deep learning for segmentation of extraocular muscles in thyroid eye disease
PURPOSE: To develop and evaluate an AI model for the segmentation of extraocular muscles (EOMs) using Magnetic Resonance Imaging (MRI). DESIGN: Single-center study with retrospective study. METHODS: The study included 52 patients with thyroid eye disease (TED) who underwent MRI examination of the orbital region at the Department of Ophthalmology, Osaka Metropolitan University, between October 2020 and June 2023. Manual labelling of all EOMs was performed on all slices. An AI model was created and compared with the manually labelled data using 5-fold cross validation on data sets of 12, 32, and 52 cases. The average signal intensity ratio (SIR) within the EOMs was measured. The primary outcome was the comparison of DICE similarity coefficients (DSC) as the agreement rate between AI's result and manual labelling amongst the three data sets (52 cases vs 32 cases vs 12 cases). The secondary outcome was the correlation of SIR between the AI and manual labelling. RESULTS: A significant difference in DSC was observed between the 12-, 32-, and 52-case data sets for the inferior rectus muscle and medial rectus muscle. A significant correlation was observed between the AI and manual labelling for SIR in all EOMs. CONCLUSIONS: An AI model was successfully developed for the automatic segmentation of EOMs. There was little measurement error between AI and manual labelling, and the AI model using 52 cases had improved measurement accuracy compared to the AI models using 32 and 12 cases.
29 May 2026
Read appraisal →Indian journal of ophthalmology
Immune checkpoint inhibitor therapy for treatment of periocular Merkel cell carcinoma: A systematic review of clinical outcomes and safety
Merkel cell carcinoma (MCC) is a rare, aggressive neurocutaneous malignancy associated with ultraviolet damage, immunosuppression, and Merkel cell polyoma virus infection. Periocular MCC is uncommon but has high rates of local recurrence and metastasis and presents unique therapeutic challenges due to functional and cosmetic considerations. Immune checkpoint inhibitor (ICI) therapy has shown efficacy in systemic MCC; however, evidence for periocular use is limited to small case series and reports without consolidated evidence or consensus. Thus, this study summarizes and evaluates the effectiveness and safety of ICIs for periocular MCC. A systematic literature search across PubMed, Embase, and Web of Science was conducted according to PRISMA guidelines. Randomized and nonrandomized studies, case series, and case reports in English describing ICI use for periocular MCC were included. Non-human studies, non-periocular MCC, and reports without ICI treatment were excluded. Extracted data included patient demographics, tumor characteristics, treatment regimen, and patient outcomes. Seven studies reporting ten patients were included. Treatment regimens consisted of pembrolizumab (n = 6) and avelumab (n = 3), or both medications (n = 1). At a mean follow-up of 11.4 ± 9.0 months, nine patients had complete response (90%); only one patient developed subsequent distant progression despite local response. Patients with both localized periocular disease and metastatic involvement demonstrated favorable responses to ICI. Adverse effects were infrequent and typically mild; only one patient discontinued therapy due to adverse effects. ICIs may be an effective and well-tolerated treatment for local and advanced periocular MCC; however, larger, prospective studies are needed.
28 May 2026
Read appraisal →Neurology
Glucagon-like Peptide-1 Receptor Agonists and Risk of Nonarteritic Anterior Ischemic Optic Neuropathy: Systematic Review and Meta-Analysis
BACKGROUND AND OBJECTIVES: Recent observational studies have reported conflicting evidence regarding an association between glucagon-like peptide-1 receptor agonists (GLP-1RAs), particularly semaglutide, and nonarteritic anterior ischemic optic neuropathy (NAION). We aimed to synthesize the pooled evidence assessing the association between GLP-1RA use and NAION risk. METHODS: Embase, Medline, and Cochrane CENTRAL databases were searched from inception to April 5, 2025. Observational studies and randomized controlled trials comparing NAION risk in GLP-1RA users with non-GLP-1RA users were included. Title/abstract and full-text screening were conducted in duplicate by 2 independent reviewers. Discrepancies were resolved through discussion or adjudication by a third reviewer. Risk of bias was assessed using the Risk of Bias In Nonrandomized Studies of Interventions (ROBINS-I) tool, and the certainty of evidence was evaluated using the Grading of Recommendations Assessment, Development, and Evaluation framework. Pooled relative risks (RRs) were estimated using Bayesian random-effects models, incorporating half-normal, between-study, and weakly informative priors. The RR with 95% credible interval (CrI) was computed to analyze the development of NAION associated with GLP-1RA exposure. RESULTS: Five studies were included in the primary analysis, encompassing 1,593,554 patients (682,456 semaglutide users and 911,098 non-GLP-1RA users). Semaglutide use was associated with a high probability of increased risk of NAION compared with non-GLP-1RA use (RR: 2.52, 95% CrI [1.56, 4.72], I2: 57.8%, P(RR > 1): 99.9%). This association was particularly pronounced among patients with diabetes (RR: 2.41, 95% CrI [1.57, 4.10], I2: 47.2%, P(RR > 1): 99.9%). The overall incidence of NAION across 5 studies and 7 comparisons, encompassing 1,460,760 patients (semaglutide: 1,108,542; dulaglutide: 326,282; and exenatide: 25,936) and 272 NAION events (semaglutide: 198; dulaglutide: 54; and exenatide: 20) was 85 cases per 100,000 GLP-1RA users (95% CrI [29, 263], I2: 98.8%, P(incidence >0.00001): 100%). The incidence of NAION across 1,108,542 semaglutide users was 118 cases per 100,000 (95% CrI [32, 451], I2: 98.8%, P(incidence >0.00001): 100%). Leave-one-out sensitivity analyses consistently supported these findings. DISCUSSION: Low-to-moderate certainty evidence indicates that semaglutide significantly increases risk of NAION relative to non-GLP-1RAs, particularly among patients with diabetes. These findings warrant further investigation and should inform clinical risk-benefit discussions.
26 May 2026
Read appraisal →Journal of neuro-oncology
Therapeutic effects of selumetinib on diffuse neurofibroma and optic pathway glioma in neurofibromatosis type 1.
PURPOSE: Selumetinib is approved for the treatment of inoperable plexiform neurofibromas (PN) in patients with neurofibromatosis type 1 (NF1). However, its efficacy in treating NF1-associated diffuse neurofibromas (NF1-DN) or optic pathway gliomas (NF1-OPG) remains unclear. We evaluated the efficacy and safety of selumetinib in these subgroups. METHODS: This was a sub-analysis of a Korean phase II open-label trial focusing on non-target treatment effects on NF1-DN and NF1-OPG. A total of 88 pediatric and adult patients with NF1-PN (59 children and 29 adults) in this trial had been treated for at least 2 years (~ 26 cycles, 28-day cycle) with oral selumetinib (20 or 25 mg/m², or 50 mg/dose every 12 h). Tumor volume, quality of life (QoL), and visual acuity were assessed. RESULTS: Among the 88 included patients, NF1-DN was diagnosed in 25 (28%), and NF1-OPG in 3 (3%). All NF1-DN patients exhibited disfigurement, two experienced pain, and a partial response (PR; ≥20% tumor reduction at a single time) was achieved in 9 of these cases (36%). The median time to PR was 6 cycles (range, 6-12), and the median time to best response was 18 cycles (range, 6-26), with a median volume change of - 11.9% (range, - 55.4% to + 36.3%). Confirmed PR (cPR; PR sustained for > 6 cycles) was observed in 6 NF1-DN patients (24%), stable disease (SD) was observed in 9 of these patients (36%), and progressive disease (PD) in 10 cases (40%). In a paired comparison, cPR was significantly lower for NF1-DN than for NF1-PN (24% vs. 88%, P < 0.001), and the median best volume reduction was also smaller (- 11.9% vs. -42.1%, P < 0.001). For the 3 NF1-OPG patients, visual impairment was present in all cases at baseline. One patient achieved PR at cycle 12 (- 36.1% from baseline) and complete response (CR) at cycle 26 but the visual acuity did not improve in this case. SD was maintained in the other two patients. Within these subgroups, QoL improved in 16 patients (57%) by cycle 26. No serious adverse events or treatment discontinuations were reported. CONCLUSIONS: Selumetinib shows therapeutic potential against NF1-DN, although with a lower response than that observed against NF1-PN. The NF1-OPG findings herein for this drug were exploratory only because of the small number of cases, but they may provide an additional clinical context for evaluating the broader effects of selumetinib across NF1-associated lesions. TRIAL REGISTRATION INFORMATION: KCT0003700, cris.nih.go.kr/cris/search/detailSearch.do/19,080, first submission March 4, 2019; first patient enrolled May 14, 2019.
25 May 2026
Read appraisal →Journal of medical systems
Advances in Technology-Assisted Visual Rehabilitation Therapies: Scoping Review
Visual impairment (VI) is a growing condition associated with aging, neurological diseases, and chronic eye conditions. In 2023, more than 2.2 billion people experienced VI, and at least 1 billion could have been prevented or remain untreated. Visual rehabilitation (VR) is essential for optimizing residual vision and promoting functional independence; however, only a minority have access to these services. In recent years, the development of technologies such as artificial intelligence (AI), mobile applications, and virtual reality (VRt) has transformed VR, offering accessible and personalized alternatives. This scoping review aimed to map and categorize the available evidence on technology-assisted visual rehabilitation therapies, identifying the types of technologies used, their applications, and reported clinical and functional outcomes. A scoping review was conducted following the Joanna Briggs Institute (JBI) methodology and PRISMA-ScR guidelines to map the literature published between 2015 and 2025 on technology-assisted VR therapies in adults with VI worldwide. Twenty-five studies, mainly from developed countries, were included. Interventions included videoconferencing, computerized systems, portable devices, VRt, artificial vision (AV), and smart sensors. Overall, digital technologies were shown to improve visual function, autonomy, and quality of life, with high patient satisfaction and adherence. Telerehabilitation (TR), understood as the remote delivery of visual rehabilitation services through videoconferencing and digital monitoring platforms, has emerged as a viable and cost-effective strategy. In contrast, immersive virtual reality technologies (VRt), head-mounted display systems, and multisensory integration devices have demonstrated potential to stimulate neuroplasticity and enhance functional visual outcomes.Telerehabilitation platforms, immersive virtual reality systems, portable electronic vision enhancement devices, artificial vision technologies, and sensor-based monitoring tools are emerging as promising interventions. These technologies have demonstrated improvements in visual acuity, reading performance, visual field sensitivity, patient adherence, and functional autonomy, although most studies remain small-scale and geographically concentrated in high-income settings.
23 May 2026
Read appraisal →Journal of medical systems
RetCond: A Conditional Diffusion Model for Self-Explanatory Multi-Class Fundus Image Classification
Vision impairment remains a major global health challenge, and early diagnosis of retinal diseases is essential to prevent avoidable vision loss. Color fundus photography is widely used for retinal assessment, but manual interpretation is time-consuming and prone to inter-clinician variability. Although deep learning has improved automated retinal disease detection, most classification models lack transparency. We introduce RetCond, a self-explanatory generative classifier designed to provide both accurate predictions and counterfactual visual explanations. RetCond is a diffusion model repurposed as a classifier whose generative capabilities enable counterfactual synthesis for built-in explanations. These counterfactual images depict the retinal changes associated with alternative diagnoses, offering insight into the model's decisions. We curated a diverse dataset of 19,565 images containing five retinal disease conditions to train and evaluate the model. Performance is assessed using standard classification metrics and by qualitatively and quantitatively evaluating the self-explanatory component. Comparisons are made against strong discriminative baselines. RetCond achieves a classification accuracy of 96.98%, comparable to RETFound (96.99%). In contrast to standard models, RetCond produces counterfactual images that highlight condition-specific retinal features, confirming its learned concepts and demonstrating its self-explanatory properties. These results show that RetCond matches state-of-the-art discriminative performance while addressing limitations of post-hoc interpretability techniques in standard deep learning models. RetCond, therefore, constitutes a contribution toward more trustworthy AI solutions in ophthalmology.
23 May 2026
Read appraisal →ACS sensors
Diagnosis of Primary Open-Angle Glaucoma Using Spectral Profiling of Aqueous Humor-Derived Exosomes
Primary open-angle glaucoma (POAG) is one of the most common neurodegenerative diseases that cause irreversible optic nerve damage. POAG diagnosis requires multimodal assessments; however, current methods like intraocular pressure (IOP) and optical coherence tomography (OCT) imaging suffer from low sensitivity and inter-patient variability, respectively. Liquid biopsy provides objective molecular signatures, offering a robust alternative to overcome limitations of conventional clinical hallmarks. Here, we present an efficient and highly sensitive diagnostic platform operating on spectral profiles from aqueous humor (AH)-derived exosomes. Following morphological and compositional validation of exosome presence in AH samples, antibody-functionalized substrates were fabricated for selective capture. Immobilized AH-derived exosomes are then coated with gold nanoparticles to generate molecular fingerprint Raman signals. A total of 7600 spectra are acquired and used to train and evaluate the convolutional neural network (CNN) model for binary classification between healthy controls and glaucoma patients. The trained CNN model achieved an AUC of 0.96 and a prediction accuracy of 91%. The system enables rapid, individualized diagnosis, overcoming sample volume limitations via integrated immunoassay-based isolation and artificial intelligence (AI)-driven classification, highlighting the potential of ocular fluid-derived EVs in the diagnosis of neurodegenerative diseases.
23 May 2026
Read appraisal →The Cochrane database of systematic reviews
Artificial intelligence for detection and staging of Alzheimer's disease using retinal images
This is a protocol for a Cochrane Review (diagnostic). The objectives are as follows: To determine the diagnostic accuracy of AI algorithm-based retinal image reading for detecting referral-requiring Alzheimer's disease in adults who have behavioral symptoms or cognitive decline in the primary care setting. Secondary objectives Where data are available, we will investigate heterogeneity by incorporating covariates in a metaregression.
22 May 2026
Read appraisal →The British journal of ophthalmology
Impact of artificial intelligence on the availability, accessibility, acceptability and quality of ophthalmic disease screening services: a scoping review
This scoping review examines the existing literature on the application of artificial intelligence (AI) in screening for eye diseases, with a focus on evaluating whether AI-assisted diagnostic technologies enhance the availability, accessibility, acceptability and quality of screening services. 42 original studies were selected for in-depth analysis, including those employing health economic evaluations. Methodological quality was assessed using the Mixed Methods Appraisal Tool, with the majority of studies demonstrating high quality-24 scored 5/5, 15 scored 4/5 and the rest scored 3/5. Among the included studies, 34 compared manual screening with either AI-assisted or fully AI-driven approaches. Availability was the most frequently studied aspect (28 studies), followed by acceptability (12 studies), whereas accessibility and service quality were less commonly addressed. Overall, AI shows significant potential to improve the cost-effectiveness of eye care services and enhance patient access, particularly in remote or underserved regions. It was also well-accepted by patients, with high satisfaction and improved referral compliance. The findings suggest that AI holds promise for advancing eye disease screening, although large-scale, long-term trials are needed to effectively integrate AI into the reconstruction of screening processes and the reshaping of eye health service systems.
22 May 2026
Read appraisal →Scientific reports
RDE-DR: robust deep ensemble CNNs for automated diabetic retinopathy detection from fundus images
Diabetic retinopathy (DR) is a leading cause of preventable blindness, motivating the development of reliable automated screening systems. This work proposes a Robust Deep Ensemble for Diabetic Retinopathy detection (RDE-DR) by analyzing ensemble fusion strategies. Four pre-trained convolutional neural networks (ResNet50, VGG16, VGG19, and DenseNet121) are trained using CLAHE-enhanced APTOS 2019 fundus images and integrated through seven heterogeneous fusion mechanisms, including voting-based, rank-based, and fuzzy-integral-inspired strategies. A consistent evaluation protocol is adopted, incorporating threshold optimization and probabilistic calibration analysis to validate robustness, decision margins, and accuracy-precision trade-offs. Experimental results show that multiple fusion techniques achieve comparable high performance and stable behavior on the APTOS 2019 benchmark, with the best configuration reaching 98.64% accuracy, 98.40% precision, 98.92% recall, 98.66% F1-score, and 99.78% Area-Under-Curve (AUC). Beyond peak accuracy, the study provides insights into ensemble reliability, calibration characteristics, and practical design choices for medical image classification systems. These results show that integrating transfer learning with CLAHE preprocessing and ensemble fusion yields stable experimental performance on the APTOS 2019 benchmark, suggesting potential for future medical decision support.
18 May 2026
Read appraisal →Clinical cancer research : an official journal of the American Association for Cancer Research
Efficacy and Safety of Belantamab Mafodotin with Bortezomib plus Dexamethasone in Patients with Relapsed/Refractory Multiple Myeloma: The DREAMM-6 Arm B Trial
PURPOSE: The phase I/II DREAMM-6 arm B study (NCT03544281) explored belantamab mafodotin combined with bortezomib/dexamethasone (BVd) in relapsed/refractory multiple myeloma (RRMM). PATIENTS AND METHODS: Adults with RRMM were enrolled sequentially in two belantamab mafodotin (intravenous) dose-escalation (DE) cohorts (2.5 then 3.4 mg/kg every 3 weeks). Additional patients enrolled sequentially to eight dose-expansion cohorts: 1.9, 2.5, or 3.4 mg/kg every 3 weeks; 2.5 or 3.4 mg/kg every 3 weeks split dose (days 1 and 8); 1.9 or 2.5 mg/kg every 6 weeks; or 2.5 mg/kg in cycle 1 stepped down to 1.9 mg/kg every 6 weeks thereafter. Patients received bortezomib twice weekly and dexamethasone four times weekly. Endpoints were dose-limiting toxicities (DLT; DE), adverse events (AE), serious AEs (SAE; DE and expansion), overall response rate (ORR; expansion), and pharmacokinetics. RESULTS: One hundred seven patients (median 4 prior lines of therapy) received BVd (n = 12-18/cohort). The median follow-up was 15.2 to 25.4 months. No DLTs occurred during DE. The most common grade 3/4 AE was keratopathy (53%). Protocol-defined ocular events (change in best corrected visual acuity and/or corneal examination findings) were reported in 93% of patients (grade 3/4: 77%). Twenty-eight (26%) patients experienced any study treatment-related SAEs; 3 of 7 fatal SAEs had a treatment-related primary cause. The ORR was 70% (95% confidence interval, 60.5-78.6). Higher initial exposures had higher probabilities of response and ocular events; lower exposures resulted in fewer deep responses, with small differences in ocular events. CONCLUSIONS: BVd demonstrated manageable safety and clinical activity across all dosing cohorts in heavily pretreated RRMM, supporting the 2.5 mg/kg every 3 weeks dose.
17 May 2026
Read appraisal →Neurosurgical review
Safety profile and surgical outcomes of the endoscopic transorbital approach as a skull base surgical corridor: a systematic review and meta-analysis
Endoscopic techniques have enabled minimally invasive approaches in neurosurgery, providing shorter recovery times and favorable outcomes. Among these, the endoscopic transorbital approach (ETOA) has emerged as a versatile surgical modality. Despite the growing body of evidence, complication rates associated with ETOA have not yet been systematically evaluated. We systematically searched PubMed, Embase, Scopus, and Web of Science up to March 2026. We included studies enrolling ≥ 5 patients who underwent ETOA as the sole surgical modality to treat both skull base and orbital lesions, providing data on early or long-term complications. A single-group meta-analysis was performed using a random-effects model with 95% confidence intervals. Heterogeneity was assessed with the I² statistic and further explored through Baujat plots and sensitivity analyses. A total of 11 observational studies, comprising 269 patients, were included. Overall, 21 different pathologies were reported. Meningioma represented the most frequent lesion (60.6% of cases), followed by schwannoma (12.0%), cavernous hemangioma (4.6%), and glioma (2.7%). Mean follow-up was 27.6 ± 15.1 months. CSF leak was observed in 1% (95% CI: 0.00 to 0.04, I² = 46.5%), and wound infection was observed in 3% (95% CI: 0.01 to 0.07, I²=0%). Ptosis occurred in 4% (95% CI: 0.00 to 0.14, I²=79.4%) and diplopia occurred in 6% (95% CI: 0.01 to 0.14, I²=68.9%). Medial gaze palsy occurred in 9% (95% CI: 0.04 to 0.18, I²=8.8%). Improvement in visual function was seen in 47% (95% CI: 0.22 to 0.73, I²=88.7%). Visual dysfunction occurred in 1% (95% CI: 0.00 to 0.04, I²=48.6%). Transient facial numbness occurred in 16% (95% CI: 0.09 to 0.25, I²=35.0%). Mortality was 0% (95% CI: 0.00 to 0.02, I²=28.7%). ETOA is a safe and promising technique for managing a wide range of skull base and orbital lesions. Future prospective and comparative studies are needed to refine indications and validate its long-term efficacy.
14 May 2026
Read appraisal →Cerebellum (London, England)
Quantitative Ocular Motor / Vestibular Assessment in Patients with Spinocerebellar Ataxia Type 3 (SCA3, Machado Joseph Disease) - Systematic Review of the Literature
Spinocerebellar ataxia type 3 (SCA3 or Machado-Joseph disease) is the second most common autosomal-dominant hereditary ataxia and often presents with ocular motor and vestibular impairment. The value of ocular motor testing for early disease detection, monitoring disease progression and as a potential treatment biomarker has been acknowledged in several studies. This systematic review aims to summarise the current evidence supporting quantitative eye movement testing in SCA3. Studies reporting on quantitative ocular motor and/or vestibular measurements in SCA3 were searched for in MEDLINE and Embase. Thirty studies reporting on 432 SCA3 patients (including 64 pre-symptomatic carriers) were included. Information on various ocular motor parameters were extracted and correlations with other parameters (including clinical scales, imaging, and genetic testing) were assessed. Ocular motor abnormalities observed comprised (horizontal) pursuit eye-movement (PEM) gain reduction (14/19 patients), saccadic intrusions (principally square-wave jerks (SWJ, 29/45)), gaze-evoked nystagmus (GEN, 65/82), and rebound nystagmus (8/10). Additionally, saccades to target displayed increased latency (13/29), reduced peak velocity (16/49) and dysmetria (33/49). Vestibular deficits measured included vestibulo-ocular reflex (VOR) gain-reductions during quantitative head-impulse testing (qHIT, 117/133) and abnormal responses during caloric irrigation (7/9), impaired visually-enhanced VOR-responses (5/6), and impaired cervical (19/66) and ocular (10/14) vestibular-evoked myogenic-potential testing. In summary, impaired VOR-gains (qHIT), GEN, horizontal PEM-gain decreases, dysmetric saccades, and SWJ were most frequently reported and therefore seem most suitable as potential disease markers. The quantitative ocular motor/vestibular assessment may facilitate early diagnosis, distinction of disease stages and also potentially allow monitoring of disease progression and treatment response.
14 May 2026
Read appraisal →PloS one
Using deep learning to identify inherited retinal diseases based on wide-field retinal imaging data
OBJECTIVE: To evaluate a novel image-based deep learning method for the automated identification of inherited retinal diseases (IRDs) and to explore the feasibility of predicting selected causative gene groups using a multimodal analysis of wide-field fundus autofluorescence (FAF) and pseudocolor fundus (pCF) images. DESIGN: The method was evaluated using a retrospective dataset of patient studies containing FAF and pCF images, as well as genetic tests for IRD. PARTICIPANTS: Patients with confirmed IRD for which both wide-field FAF and pCF images and genetic tests for IRD performed at Hadassah University Medical Center were included. The dataset consisted of 409 patients (330 patients with IRD with the 25 most commonly affected genes in our population and patients without IRD, and 79 patients without IRD). METHODS: Nine EfficientNet-V2-m convolutional neural networks were trained for the following three classification tasks: a binary IRD vs. non-IRD classification, and classification into two groups of five causative genes (Groups 1 and 2). For each task, three models were trained on the FAF images only, the pCF images only, and both the FAF and pCF images. The performance of the models was then evaluated and compared using 5-fold cross-validation. MAIN OUTCOME MEASURES: Accuracy, precision, F1 scores, AUC, and confusion matrices. RESULTS: The multimodal classification models that were trained on both the FAF and pCF images yielded the best results. The binary classification model had a mean (±SD) accuracy of 0.95 ± 0.01, a mean precision of 0.92 ± 0.01, and a mean F1 score of 0.90 ± 0.02. The Group 1 classification model had a mean accuracy of 0.92 ± 0.03, a mean precision of 0.93 ± 0.03, and a mean F1 score of 0.89 ± 0.03. Finally, the Group 2 classification model had a mean accuracy of 0.85 ± 0.03, a mean precision of 0.87 ± 0.04, and a mean F1 score of 0.83 ± 0.04. CONCLUSIONS: Our results indicate that determining whether a patient has IRD can be performed with high accuracy within this retrospective cohort based on FAF and pCF images using image-based deep learning classifiers. This image-based approach may assist clinicians during the patient's initial visit by providing decision support prior to genetic testing. It may also help prioritize patients for genetic workup, particularly in settings in which genetic testing is not readily available. Further prospective and external validation is required before clinical implementation.
13 May 2026
Read appraisal →Blood advances
Belantamab mafodotin, carfilzomib, lenalidomide, and dexamethasone for relapsed or refractory multiple myeloma
Belantamab mafodotin (belamaf), an antibody-drug conjugate targeting B-cell maturation antigen, has demonstrated single-agent activity in relapsed/refractory multiple myeloma (RRMM) but is associated with ocular toxicity. This phase 1/2 study evaluated the safety and preliminary efficacy of belamaf administered every 8 weeks in combination with carfilzomib, lenalidomide, and dexamethasone (KRd-b) in patients with RRMM. Eligible patients had received at least 1 previous line of therapy and had not progressed on full-dose lenalidomide. In the dose-escalation phase (3+3 design), belamaf was administered at 1.4 or 1.9 mg/kg every 8 weeks. The recommended phase 2 dose was 1.9 mg/kg, with no dose-limiting toxicities at this level. Among 19 patients treated, the overall response rate was 89.5%, and 78.9% achieved very good partial response or better, with minimal residual disease negativity in all complete responders. At a median follow-up of 19.3 months, 24-month progression-free survival and overall survival rates were 74.3% and 85.1%, respectively. Keratopathy occurred in 94.7% of patients but was mostly grade 1 to 2, reversible, and manageable without permanent discontinuation. Grade ≥3 keratopathy (31.6%) was comparable with historical rates despite the less frequent dosing. Other adverse events, including hematologic and gastrointestinal toxicities, were manageable and consistent with known profiles of KRd or belamaf. The combination demonstrated deep and durable responses, including in patients at high-risk and with lenalidomide maintenance dose-refractory disease. These results support the feasibility of KRd-b with extended-interval belamaf and warrant further evaluation in phase 2 trials to confirm its clinical benefit in RRMM. This trial is registered at www.clinicaltrials.gov as NCT04822337.
13 May 2026
Read appraisal →Scientific reports
Automated phenotyping of ophthalmologic diseases from routine medical records using small language models and the human phenotype ontology (HPO)
Automated phenotyping in ophthalmology requires accurate standardization of clinical terms to facilitate interoperability and research. This study evaluates the suitability of the human phenotype ontology (HPO) for automated extraction of ophthalmic phenotypes from narrative documentation. We developed a locally operated AI pipeline combining text segmentation and negation detection based on a small language model (PHI-4) with a dense retrieval approach using an augmented multilingual HPO catalog. Synonyms were incorporated into the HPO during training on anonymized consecutive physician letters. To validate the pipeline, 175 anterior segment and fundus descriptions from randomly picked medical records were manually annotated with HPO terms as ground truth. Overall, 342 HPO terms were identified manually (on average 2.53 terms per document), with 341 retrieved by the pipeline (on average 2.52 terms per document). Performance metrics showed a median Jaccard similarity of 0.67, precision of 0.83, recall of 0.82, and F1 score of 0.80. These results demonstrate that our AI pipeline effectively extracts standardized HPO terms from free-text ophthalmic findings. Integration of this pipeline into clinical information systems may enhance data interoperability and reduce manual coding workload in ophthalmology practices in the future.
12 May 2026
Read appraisal →Journal of ethnopharmacology
Senecio scandens Buch. - Ham.: A comprehensive review of botany, phytochemistry, biological activity, toxicity, quality control, application, and practical domain.
ETHNOPHARMACOLOGICAL RELEVANCE: Senecio scandens Buch.-Ham., a medicinal herb from the Asteraceae family, contains flavonoids, terpenoids, and alkaloids as its primary bioactive compounds. It is commonly used in the treatment of ocular diseases and dermatological conditions. However, with its expanding applications across various fields, growing attention has been directed toward the potential safety concerns associated with its use. AIM OF THE REVIEW: This article systematically reviews existing literature on S. scandens, examining its botany, phytochemistry, biological activity, toxicity, applications, and development utilization, to provide a theoretical basis for in-depth exploration of its pharmacological mechanisms and resource development. MATERIALS AND METHODS: This study conducted a systematic review of literature on S. scandens published from 1972 to 2025, based on databases including PubMed and Web of Science. Plants names are provided in accordance with "The Plant List" (https://wfoplantlist.org/). RESULT: Over 200 chemical constituents, primarily flavonoids, terpenoids, and alkaloids, have been isolated and identified from S. scandens, endowing it with diverse biological activities such as anti-inflammatory, antibacterial, antioxidant, and anti-tumor effects. To address potential hepatorenal toxicity from its pyrrolizidine alkaloids, relevant safety guidelines have been gradually established alongside various clinical dosage forms. It is also widely used in fields including medicine, animal husbandry, agriculture, and cosmetics. CONCLUSION: By systematically reviewing the botany, phytochemistry, biological activity, toxicity, application, and development and utilization of S. scandens, this paper identifies key utilization obstacles, proposes future research directions, and provides a theoretical framework for its further development.
12 May 2026
Read appraisal →Journal of medical Internet research
AI in Clinical Decision Support Systems: Promising Applications and Strategies for Managing Data Challenges
The translation of big data analytics and artificial intelligence (AI) into clinical decision support systems (CDSSs) has advanced from proof of concept to real-world clinical practice. AI-informed CDSSs show measurable improvements in diagnostic accuracy, risk stratification, resource use, and patient outcomes compared to traditional models, offering the potential to assist clinicians in managing symptom complexity and uncertainty in health care delivery. Despite this potential, access to large amounts of high-quality and granular data remains one of the most significant bottlenecks to AI-enabled CDSSs. We argue that as health care systems increasingly adopt data-driven decision support, addressing the challenges of data accessibility and protection is essential to realizing the full potential of AI in clinical medicine. We use selected case examples of AI-informed CDSSs in oncology, organ transplantation, diabetic retinopathy, epilepsy, spinal cord injury, rare disease diagnosis, and emergency medicine to illustrate opportunities and challenges related to AI's potential to improve patient outcomes. We discuss public and semipublic, medical institutional and commercial, and government and national data sources that are currently available for the development of CDSSs and highlight the practical and ethical constraints associated with these data. We consider alternative data resources and ways in which health care systems can strengthen data ecosystems to increase AI-driven CDSS efficacy and implementation to improve patient outcomes.
11 May 2026
Read appraisal →Scientific reports
From intelligent models to clinical tools: the evolving landscape of AI in medical imaging
Integration of Artificial Intelligence (AI), particularly deep learning, into medical imaging represents a profound shift in diagnostic medicine, moving from purely descriptive analysis to advanced predictive and prescriptive analytics. This Collection explores the rapid advancement of AI-driven tools in their specific fields such as oncology, cardiology, ophthalmology and so on, highlighting their potential to improve diagnostic accuracy, workflow efficiency, and personalized treatment planning. However, significant challenges remain, including the heterogeneity of medical image data, the "black box" nature of some intelligent models, and the critical hurdles of clinical integration and validation. The research presented here addresses these frontiers, showcasing innovations in algorithm development, explainable AI, and translational application. This Editorial synthesizes the contributions and outlines the essential collaborative pathway-uniting computer scientists, clinicians, and regulatory bodies-required to translate algorithmic promise into robust, trustworthy, and equitable clinical tools that genuinely improve patient care.
9 May 2026
Read appraisal →Journal of biomedical informatics
Model utility and explainability in federated learning - A case study in healthcare using fundus oculi datasets
OBJECTIVE: Introduce a case study for Federated Learning (FL) in healthcare, addressing challenges posed by patient privacy and limited large-scale datasets. Our goal is to assess the features learned by FL methods in a simulated, diverse setting that emphasizes realistic data heterogeneity, and to analyze the learned representations for their medical relevance using both local and global explainability techniques. METHODS: Six fundus oculi datasets were combined to simulate a diverse federated learning environment, representing heterogeneous data conditions. We evaluated three established FL methods against centrally trained models, assessing both predictive performance and the learned representations. Specifically, explainability techniques were employed to examine the features learned by the models, and local explanations were evaluated against attention maps annotated by ophthalmologists. Robustness against common biases in fundus datasets was also assessed. RESULTS: Our study found improvements in model utility (up to 9.97%) with FL methods compared to isolated training. Analysis of learned representations revealed that federated models predominantly learn the vertical cup-to-disc ratio, a crucial feature for glaucoma diagnosis, and demonstrated robustness against common biases. High agreement was observed between local explanations and ophthalmologist-annotated attention maps. CONCLUSION: This study demonstrates the benefits of FL systems in a healthcare scenario, providing a case study for evaluating federated systems beyond idealized benchmarks. Our findings highlight the potential of FL to not only improve model utility in privacy-sensitive medical domains but also to learn medically relevant features instead of spurious correlations.
9 May 2026
Read appraisal →Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
Comparing the performance of four mainstream large language models on medical literature review generation: a human expert evaluation in SMILE surgery
PURPOSE: To systematically evaluate and compare the performance of four leading large language models (LLMs) in generating medical literature reviews across topics of varying research maturity, thereby providing insights for their effective and responsible application in academic writing. METHODS: In this comparative study, using standardized prompts, we instructed four leading LLMs (GPT-4, Gemini 2.5 Pro, Grok-3, and DeepSeek R1) to generate literature reviews on nine topics related to small incision lenticule extraction (SMILE) surgery. These topics were categorized into three groups by research maturity: well-researched, controversial, and open. Seven ophthalmology experts evaluated the generated content across four dimensions: quality, accuracy, bias, and relevance, while all references were verified for authenticity. Performance differences among models were evaluated using group comparison tests followed by post-hoc analysis. RESULTS: Significant performance variations were identified across all four models and dimensions (p < 0.001). Specifically, Gemini ranked highest in content quality, accuracy, and bias control. In contrast, DeepSeek, despite its high-quality score, received the lowest relevance score. Grok-3 demonstrated the highest reference authenticity (p < 0.001), whereas GPT-4's was the lowest (p < 0.001). All models showed diminished performance on open topics and exhibited severe reference fabrication ("hallucinations"). CONCLUSION: Rather than excelling universally, LLMs exhibit distinct and task-specific strengths that mandate a task-driven, hybrid strategy in tool selection. Reference fabrication was found to be a pervasive issue across all models, regardless of the task topic, elevating human verification from a best practice to an essential safeguard for academic integrity.
9 May 2026
Read appraisal →Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
Automatic measurement and evaluation of anterior segment anatomical structures via UBM images using a deep learning-based approach
PURPOSE: To develop a deep-learning model capable of measuring essential anterior segment (AS) parameters derived from preoperative ultrasound biomicroscopy (UBM) images of candidates for implantable collamer lens (ICL) surgery. SETTING: Tianjin Medical University Eye Hospital, Tianjin, China. DESIGN: Cross-sectional retrospective study. METHODS: A dataset comprising 1,480 preoperative panoramic UBM images taken from 638 eyes of 320 subjects was collected and was divided into training and testing subsets at a proportion of 7:3. Using the YOLOv8-pose algorithm, the model identified ten anatomical key point coordinates and computed six relevant AS parameters. Both manual and anterior segment optical coherence tomography-based non-contact measurements served as reference standards for evaluating the model's accuracy. The relationship between postoperative vault and preoperative parameters measured by the model was analyzed using multiple linear regression. RESULTS: On the test dataset, the model achieved an intraclass correlation coefficient (ICC) exceeding 0.978, with a mean Euclidean distance of 67.65 ± 54.25 μm across all point locations. The ICC values for anterior chamber depth (ACD), pupil diameter, and sulcus-to-sulcus distance were above 0.980 (95% CI: 0.975 to 0.985), with average relative error below 1.7%. Additionally, postoperative vault at one month was significantly correlated with model-measured parameters, including crystalline lens rise, iris concavity, and ACD (P < 0.001). CONCLUSION: This study introduces a robust program capable of quantitatively measuring AS parameters with accuracy comparable to that of experienced ophthalmologists. The findings provide valuable guidance for ICL sizing and vault prediction.
5 May 2026
Read appraisal →Experimental eye research
Automated segmentation of pterygium lesions using multiscale deep learning networks
Pterygium is an eye condition that needs to be identified at an early stage so that its progression can be mitigated in order to avoid the possible threat of visual impairment. One of the important low-level modules in determining the severity level of the pterygium automatically is to measure the extent of fibrovascular tissue encroachment onto the corneal and pupil regions. Therefore, it is important to develop a semantic segmentation method to map and quantify the lesion tissues accurately, especially through the deep learning technique. As the pterygium condition progresses from the trace stage to the severe stage, the lesions will generally become bigger but maintain more or less the same shape, which looks like a wedge. Taking inspiration from this unique pattern of pterygium lesions, this work aims to explore multiscale deep learning networks to capture the variable scales of the lesion features effectively. All the proposed multiscale modules are embedded at the bottleneck layer of the base UNet architecture, whereby the feature map size is the smallest to reduce the potential increment in the computational burden. Apart from that, the total number of convolutional channels is fixed to 1024, whereby the multiscale module replaces the first convolutional set at the UNet's bottleneck layer. Two main multiscale modules have been explored, which are spatial pyramid pooling (SPP) and atrous spatial pyramid pooling (ASPP) modules. Furthermore, each of these modules has been expanded by analyzing the equal-flow (EF) and waterfall-flow (WF) patterns in constructing the parallel paths that represent the multiscale features. According to the simulation results, the best segmentation network is UNet, which has been embedded with three parallel paths of the EF-ASPP module that produces the lowest Hausdorff distance of 16.75 pixels. As a result, the severity level of pterygium can be better predicted through accurate extraction of the lesion map. This work can be further improved by analyzing different network architectures of the multiscale module, especially networks that focuses on the larger feature maps size, but surely with a trade-off in computational burden.
5 May 2026
Read appraisal →Microvascular research
OPU-NET-DADENA: Optimized deep learning ensemble with u-net segmentation for early detection of diabetic retinopathy
One of the most significant microvascular complications of diabetes mellitus (DM) is diabetic retinopathy (DR). In the early stages, patients with DR may not exhibit any noticeable symptoms. It is a diabetic disorder that damages retinal blood vessels in the eyes. At first, there are no symptoms or sporadic visual issues. When it worsens, it affects both eyes and can lead to partial or total blindness. A person who already has DM is therefore constantly at a higher risk of developing the condition. Early identification can prevent the possibility of total and irreversible blindness. Therefore, needs an efficient and early diagnosis system. So, this paper proposes a new deep-learning methodology in a specific Deep Siamese DenseNet for early detection of DR. The proposed method is accomplished through various steps, such as Data Collection, Preprocessing, Augmentation, Segmentation, Feature Extraction, Feature Selection, and Detection. An adaptive histogram equalization approach named Contrast Limited Adaptive Histogram Equalization (CLAHE) is used to preprocess input images, which reduces amplification of noise. Then, the segmentation is done by the Optimized U-NETs. Next, features of the contrasted retinal images are extracted by residual attention EfficientNet (RA-EfficientNet). Then, the optimal features are selected by a hybrid Reptile Search Algorithm (RORS). Finally, the deep learning methodology includes DarkNet, DenseNet 201, and NasNetMobile used to detect diabetic retinopathy at an early stage. The model is implemented in MATLAB and evaluated using accuracy, precision, F-score, specificity, sensitivity, MCC, NPV, FPR, and FNR. The proposed approach achieves 99.33% accuracy and 98.32% precision, outperforming previous methods that reported accuracies of 95-97% and precisions of 94-96%, demonstrating its effectiveness for reliable early detection of DR.
5 May 2026
Read appraisal →American journal of ophthalmology
Optical Coherence Tomography Radiomics and Machine Learning Enable Accurate Detection of Forme Fruste Keratoconus
PURPOSE: To evaluate the diagnostic performance of a radiomics-based machine learning approach applied to corneal optical coherence tomography (OCT) images for detecting forme fruste keratoconus (FFKC). DESIGN: Evaluation of machine learning diagnostic algorithms. METHODS: OCT images from 307 eyes (234 normal, 73 FFKC) were acquired along eight meridians (M1-M8). All images underwent preprocessing before texture-based radiomics feature extraction. Three machine learning classifiers-Random Forest, C5.0, and XGBoost-were trained using a feature subset selected by recursive feature elimination (RFE). Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. RESULTS: A total of 3752 features were extracted per eye, of which 41 were selected for model training. All three models demonstrated strong diagnostic performance in the test set (AUCs > 0.92), with no significant differences between models (P > .05). The XGBoost model achieved the highest performance (AUC = 0.93, 95% CI: 0.829-1.0, sensitivity 0.857, specificity = 0.978, accuracy = 0.950). Among the top 10 XGBoost features ranked by importance, a preferred meridional distribution was observed, with most features concentrated along M1 to M3, corresponding to the inferotemporal corneal region. CONCLUSION: Radiomics analysis of corneal OCT images combined with machine learning enables accurate FFKC detection using a single imaging device, providing diagnostic information beyond conventional morphological assessment and suggesting a potential imaging biomarker for early keratoconus screening.
5 May 2026
Read appraisal →Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine
Visualization of the Trochlear Nerve Using Deep Learning-enhanced 3D T2-weighted MR Imaging at 3T
PURPOSE: Cranial nerve imaging with 3T MRI commonly uses 3D fast imaging employing steady-state acquisition (3D-FIESTA); however, this sequence has limitations in achieving higher spatial resolution and provides poor tissue contrast between cranial nerves and adjacent vascular structures. We evaluated 3D T2-weighted imaging (T2-CUBE) with deep learning-based reconstruction (DLR) for cranial nerve visualization, focusing on the trochlear nerve, the smallest cranial nerve with the longest intracranial course, and compared it with T2-CUBE without DLR and 3D-FIESTA. METHODS: Ten healthy male volunteers (age, 23-40 years; mean age, 32 years) underwent T2-CUBE with and without DLR, and 3D-FIESTA at 3T. Two neuroradiologists independently evaluated trochlear nerve visualization in 4 anatomical segments (origin from the midbrain, cisternal, tentorial, and anterior portion of its cavernous segments) using a 3-point scale, and SNR of the pons (SNRPONS) and cerebrospinal fluid (SNRCSF) were calculated. RESULTS: T2-CUBE with DLR achieved a 100% visualization across all trochlear nerve segments and demonstrated significantly better visualization than both T2-CUBE without DLR and 3D-FIESTA (P < 0.025). T2-CUBE without DLR showed 67.5%-100% visualization across the 4 segments. 3D-FIESTA showed 32.5%-80% visualization of the origin from the midbrain, cisternal, and tentorial segments, with no visualization of the cavernous segments. DLR increased SNRPONS and SNRCSF by factors of 1.8-2.5 (SNRPONS: 14.1 vs 5.7; SNRCSF: 31.8 vs 17.5, respectively; P < 0.001). T2-CUBE with DLR demonstrated significantly higher SNRPONS than 3D-FIESTA (14.1 vs 6.4, P < 0.001), while SNRCSF was comparable (31.8 vs 36.3, P = 0.20). CONCLUSION: T2-CUBE with DLR at 3T provided a significantly better trochlear nerve visualization than T2-CUBE without DLR and 3D-FIESTA. This technique may extend beyond the trochlear nerve to other cranial nerves and to the evaluation of neurovascular compression in the cistern, with the potential to become the new standard for cisternal imaging.
3 May 2026
Read appraisal →Neurology
Idiopathic Intracranial Hypertension Prevalence and Hormonal Contraception: A Meta-Analysis
BACKGROUND AND OBJECTIVES: Women's health advice in people living with idiopathic intracranial hypertension (IIH) is inconsistent, primarily due to an enduring concern that hormonal contraception (HC), including oral contraceptive pills (OCPs) and hormonal intrauterine devices (IUDs), might be associated with a higher prevalence of IIH. This systematic review and meta-analysis examines whether a meaningful association exists between HC use and IIH prevalence. METHODS: Systematic searches of Ovid MEDLINE, Embase, and the Cochrane Library were conducted from their inception to May 14, 2025. Gray literature and citation lists were also reviewed. Observational studies comparing HC use among women with IIH to controls without IIH were included. Noncomparative studies and pharmacovigilance analyses were excluded. Two independent reviewers conducted data extraction, assessed the quality of studies using the Newcastle-Ottawa Scale, and appraised the certainty of evidence using the Grading of Recommendations, Assessment, Development, and Evaluation framework. A random-effects meta-analysis using the Mantel-Haenszel method in Review Manager 5.4 was conducted to evaluate the association between HC use and IIH prevalence, the primary outcome of interest, with leave-one-out sensitivity analyses and subgroup analyses stratified by HC modality. RESULTS: A total of 13 studies (nIIH = 5,351 and ncontrol = 669,260; mean age = 33.3 years; 100% female) were included. HC use was not significantly associated with IIH prevalence (odds ratio [OR] 0.93, 95% CI 0.69-1.24, p = 0.60, I2 = 82%; n = 12 studies). Subgroup analyses revealed no significant associations for OCPs (OR 0.91, 95% CI 0.72-1.16, p = 0.45, I2 = 25%; n = 6 studies), IUDs (OR 1.45, 95% CI 0.61-3.44, p = 0.39, I2 = 92%; n = 5 studies), or medroxyprogesterone acetate injections (OR 1.21, 95% CI 0.84-1.74, p = 0.30, I2 = 2%; n = 2 studies). Findings were largely robust in sensitivity analyses. Quality assessments ranged from poor to good, with the certainty of evidence rated as very low to low. DISCUSSION: The meta-analysis found no significant association between HC use and IIH prevalence, with consistent results across various HC modalities. This evidence provides confidence to reassure patients with IIH and clinicians that the directed use of HC should be continued as appropriate. The limited quality of available evidence and considerable heterogeneity highlights the necessity for large-scale, well-designed studies, particularly in diverse patient populations, to validate these findings.
3 May 2026
Read appraisal →Turkish journal of ophthalmology
Large Language Models in Ophthalmology: A Bibliographic Analysis
This study evaluated the distribution of research on the use of large language models (LLMs) in ophthalmology through a bibliographic analysis of articles retrieved from PubMed through November 2024. Studies were categorized into four main areas of LLM application: clinical decision-making (further divided according to subspecialties), education, patient interactions, and miscellaneous applications. Descriptive statistics were used to analyze the distribution of studies by ophthalmic subspecialty, geographical region, journal quality, and author characteristics, including gender and scholarly impact (h-index and i10-index). The findings revealed that clinical decision-making was the most common application (43.7%), with the majority of studies in this subgroup focusing on the retina (39.5%). Geographically, most of the research originated from North America (48.3%), followed by Asia (29.9%) and Europe (20.7%). Most studies were published in high-impact journals (Q1 journals: 74.7%), particularly for those related to clinical decision-making in retina (80.0%), glaucoma (100%), and multiple subspecialties (87.5%). Gender disparities were evident across all author roles, with female authors accounting for only 29.9% of first authors, 25.3% of last authors, and 26.4% of corresponding authors. The results suggest a need for greater diversity in terms of gender and geographic representation in LLM research in ophthalmology to promote inclusive progress in the field. Bu çalışma, PubMed’de Kasım 2024’e kadar yayımlanmış göz hastalıkları alanında büyük dil modellerinin (BDM) kullanımı ile ilgili araştırmaların bibliyografik analizini amaçlamaktadır. Çalışmalar, klinik karar desteği (yandallara göre ayrılmış), eğitim, hasta etkileşimleri ve diğer uygulamalar olmak üzere BDM yöntemlerinin dört ana uygulama alanına göre kategorize edilmiştir. Tanımlayıcı istatistikler kullanılarak çalışmaların, oftalmoloji alt uzmanlıkları, coğrafi bölge, dergi kalitesi ve yazar özellikleri (cinsiyet ve bilimsel etki, h-endeksi ve i10-endeksi dahil) açısından dağılımı analiz edilmiştir. Bulgularımız, klinik karar desteğinin en yaygın uygulama olduğunu (%43,7) ve bu alt grup içerisinde çalışmaların çoğunun retina üzerine odaklandığını (%39,5) ortaya koymuştur. Coğrafi olarak, araştırmaların çoğu Kuzey Amerika’dan (%48,3) yayımlanmakta olup sırasıyla Asya (%29,9) ve Avrupa (%20,7) Kuzey Amerika’yı izlemektedir. Çoğu çalışma, özellikle de klinik karar desteği alt grubunda, retina (%80,0), glokom (%100) ve birden fazla alt uzmanlık dalını kapsayan çalışmalar (%87,5), yüksek etki faktörüne sahip dergilerde yayımlanmıştır (Q1 dergileri: %74,7). Cinsiyet dağılımındaki eşitsizlik, tüm yazar rollerinde belirgin olarak gözlemlenmiştir. Çalışmalarda kadın yazarlar, ilk yazarların yalnızca %29,9’unu, son yazarların %25,3’ünü ve sorumlu yazarların %26,4’ünü oluşturmuştur. Sonuçlar, göz hastalıkları alanındaki BDM araştırmalarında cinsiyet ve coğrafi temsilde daha fazla çeşitliliğe ihtiyaç duyulduğunu ve bu çeşitliliğin alandaki kapsayıcı ilerlemeyi teşvik edeceğini göstermektedir.
2 May 2026
Read appraisal →PloS one
Disparity of ophthalmic surgeries in Japan
PURPOSE: Although regional disparities in access to surgical care have been reported across medical specialties, nationwide evaluations of variation in ophthalmic surgical services remain limited. This study examined prefecture-level differences in major ophthalmic surgeries in Japan using publicly available claims data. METHODS: We analyzed the ninth release of the National Database of Health Insurance Claims and Specific Health Checkups of Japan (NDB Open Data) for fiscal year 2022 (April 2022-March 2023). Prefecture-level procedure counts were extracted for cataract surgery (K282), vitrectomy (K279-K281 and related codes), glaucoma surgery (relevant K-codes), and corneal transplantation. To reduce potential underestimation in non-Diagnosis Procedure Combination settings, ophthalmology-related procedures reimbursed under the Short-Stay Surgery Basic Fee 3 (A400) were identified and incorporated according to predefined mapping rules. Surgical rates per 100,000 population and per board-certified ophthalmologist were calculated. Regional inequality was quantified using population-weighted Gini coefficients. Associations between ophthalmologist density and surgical volume were assessed using Pearson's correlation coefficients. RESULTS: Nationwide totals were 1,777,502 cataract surgeries, 154,336 vitrectomies, 80,753 glaucoma surgeries, and 2,895 corneal transplantations. Prefecture-level surgical rates per 100,000 population varied 1.8-fold for cataract surgery, 3.3-fold for vitrectomy, and 7.8-fold for glaucoma surgery. Several prefectures reported no corneal transplantation cases. Population-weighted Gini coefficients were 0.064 for cataract surgery, 0.125 for vitrectomy, 0.190 for glaucoma surgery, and 0.351 for corneal transplantation. Similar patterns were observed after adjusting for age structure, indicating that regional disparities were not solely explained by demographic differences. Ophthalmologist density varied 2.5-fold across prefectures and was positively correlated with surgical volume for cataract surgery and vitrectomy, but not for glaucoma surgery. CONCLUSIONS: Using nationwide claims data, we identified substantial regional variation in major ophthalmic surgical procedures in Japan, with greater inequality observed for more specialized surgeries. These findings provide a population-level description of ophthalmic surgical distribution and may inform future evaluations of healthcare resource allocation.
27 Apr 2026
Read appraisal →PloS one
Machine learning identifies pupil size and corneal thickness as key predictors of axial elongation rate
PURPOSE: This study aimed to develop a machine learning-based prediction model for myopia progression using ocular biometric parameters to provide an objective assessment tool for clinical practice. METHODS: A retrospective analysis was conducted on patients treated at Shanghai Parkway Health Ophthalmology Department as the training set, and myopic individuals from the Optometry Center of Peking University People's Hospital as the validation set. Demographic and biometric data were collected, including central corneal thickness (CCT), axial length (AL), corneal curvature (K-value), anterior chamber depth (ACD), corneal diameter (WTW), and pupil size (PS). Seven machine learning models (e.g., XGBoost, random forest, support vector machine) were employed for modeling, with performance optimized via 5-fold cross-validation. Model accuracy was evaluated using mean squared error (MSE) and the coefficient of determination (R²), and variable importance was analyzed. RESULTS: No statistically significant differences were observed in baseline characteristics between the training and validation sets (all P > 0.05). The XGBoost model demonstrated the best performance, achieving R² = 0.913 (MSE = 0.005) on the training set and R² = 0.766 (MSE = 0.016) on the test set. Variable importance analysis revealed pupil size (score 100) and corneal thickness (40.88) as the key predictors of axial elongation rate, followed by age of onset (17.96). CONCLUSION: The machine learning-based prediction model effectively utilizes ocular biometric data to assess myopia progression risk, with pupil size and corneal thickness identified as core predictive factors. This model provides a quantitative tool for early clinical intervention. Future studies should expand the sample size and incorporate additional biomarkers to optimize performance.
27 Apr 2026
Read appraisal →Behavior research methods
FaceTrack-AOI: An AI-driven tool for automated dynamic AOI placement and eye movement analysis in face perception studies
Face perception is fundamental to human social interaction, relying on the dynamic allocation of visual attention to facial features across natural variations in poses, expressions, lighting and context-critical factors for real-world face perception. While eye-tracking studies have advanced our understanding of this process, analyzing gaze data in naturalistic settings presents challenges due to the labor-intensive and often subjective process of manually defining areas of interest (AOI) in dynamic stimuli. We introduce FaceTrack-AOI, an open-source, artificial intelligence (AI)-driven tool that automates dynamic AOI construction using robust 68-point facial landmark detection. Featuring a user-friendly graphical interface, the tool allows researchers to define and export eye movement metrics without programming expertise. It supports both rectangular and polygon-based AOIs, with customizable buffers, blank zones, and hierarchical configurations, accommodating diverse theoretical and methodological needs. Validation on multi-ethnic naturalistic video frames, images from a standardized face dataset (Chinese Face and Body Dataset [CFBD]), and benchmarks against a published study revealed strong agreement with expert-defined manual AOIs, achieving a mean spatial overlap of 95.73%. A proof-of-concept validation confirmed high fixation capture: over 98.8% of dwell time was correctly directed toward key AOIs. In two experiments with infants (6-18 months) and adults viewing 28,800 dynamic video frames, and children (2-8 years), both neurotypical and with autism spectrum disorder (ASD), viewing 80 static faces, FaceTrack-AOI demonstrated enhanced sensitivity in detecting developmental and group differences in naturalistic face recognition. By integrating AI-powered tracking with an intuitive interface, FaceTrack-AOI streamlines gaze analysis for dynamic facial stimuli, offering researchers an efficient and scalable solution to advance face perception research in ecologically valid contexts.
26 Apr 2026
Read appraisal →The British journal of ophthalmology
Use of patient-reported outcomes in ophthalmology clinical trials between 2014 and 2023.
BACKGROUND/AIMS: Patient-reported outcomes (PROs) evaluate health and functional status, and PRO measures (PROMs) are standardised tools for measuring PROs. Together, they provide valuable insights into treatment efficacy, safety and practicality not captured by traditional clinical endpoints. This cross-sectional analysis with a systematic search component aims to investigate the use, interpretation and reporting of PROs and PROMs in ophthalmic randomised controlled trials (RCTs). METHODS: Ophthalmic RCTs published in the top 10 highest impact factor ophthalmic and medical journals between 2014 and 2023 were systematically reviewed. The frequency of PRO inclusion and adherence to Consolidated Standards of Reporting Trials (CONSORT) PRO guidelines was assessed. The relationship between PRO utilisation and study-level and journal-level characteristics was explored with multivariable regression. RESULTS: Among 9436 records screened, 333 RCTs met eligibility criteria. Of these, 87 (26.1%) included PROs, and 28 (8.4%) used them as primary outcomes. Most studies (83/87, 95.4%) leveraged PROMs, with ophthalmology-specific tools predominating (73.5%). Minimal important differences (MIDs) were rarely used (2.3%) for PRO interpretation. At least 8/13 CONSORT PRO Extension items were reported in 33.3% studies, and trials with primary PRO endpoints had better adherence (p<0.001). PRO utilisation was less likely in trials with lower 5-year journal impact factor (adjusted OR (aOR) 0.99, 95% CI 0.98 to 1.00, p=0.037) and pharmaceutical compared with health service interventions (aOR 0.15, 95% CI 0.03 to 0.67, p=0.013). CONCLUSIONS: PRO integration and interpretation remain limited in high-impact ophthalmic RCTs, despite offering a meaningful adjunct to objective endpoints. Future trials should adopt validated and condition-specific PROMs and establish MIDs to enhance interpretation.
26 Apr 2026
Read appraisal →The British journal of ophthalmology
Ethnic and dose-dependent differences in atropine efficacy for myopia control: a systematic review and meta-analysis
BACKGROUND/AIM: Clinical uncertainty remains regarding optimal atropine concentration, treatment duration and potential differences in efficacy for myopia control between Asian and non-Asian children. This systematic review and meta-analysis evaluated the efficacy of different concentrations of atropine for myopia control, comparing outcomes among East Asian, South Asian and white European children. METHODS: Five databases were searched for randomised controlled trials (RCTs) including children ≤16 years with myopia who received atropine treatment. Thirty-four RCTs with ≥12 months of follow-up were included. Weighted mean differences (WMD) in spherical equivalent refraction (SER) progression and axial length (AL) elongation were pooled by atropine concentration and ethnicity. RESULTS: Compared with controls, atropine significantly reduced myopia progression across all concentrations: <0.1% (WMD in SER: 0.44 (95% CI 0.35 to 0.52) dioptres (D)/year; AL: -0.20 (95% CI -0.24 to -0.16) mm/year), 0.1% to <0.5% (0.81 (95% CI 0.50 to 1.13) D/year) and ≥0.5% (1.06 (95% CI 0.88 to 1.24) D/year; -0.36 (95% CI -0.40 to -0.33) mm/year). The pooled effect on SER and AL progression across all concentrations was greater in East Asians (0.63 (95% CI 0.50 to 0.76) D/year; -0.26 (95% CI -0.31 to -0.20) mm/year) than in South Asians (0.40 (95% CI 0.11 to 0.70) D/year; -0.13 (95% CI -0.21 to -0.05) mm/year) or white Europeans (0.18 (95% CI 0.11 to 0.25) D/year; -0.11 (95% CI -0.16 to -0.05) mm/year). CONCLUSION: Atropine slows myopia progression in a dose-dependent manner in studies with 1-5 years of follow-up. Efficacy appears greater in Asian children, particularly East Asians, who also exhibit greater photopic pupil dilation. These findings support the role of atropine in myopia control and highlight the importance of ethnicity-specific considerations when prescribing and tailoring treatment strategies. PROSPERO REGISTRATION NUMBER: CRD42023454104.
26 Apr 2026
Read appraisal →International ophthalmology
Topical ROCK inhibition accelerates recovery after Descemetorhexis Without Endothelial Keratoplasty (DWEK) in Fuchs endothelial corneal dystrophy: a prospective comparative clinical trial
PURPOSE: To compare the clinical efficacy and recovery kinetics of Descemetorhexis Without Endothelial Keratoplasty (DWEK) performed with and without adjunctive topical ripasudil 0.4% in eyes with Fuchs endothelial corneal dystrophy (FECD). SETTING: Cornea and Refractive Surgery Services, Guru Nanak Eye Centre, Maulana Azad Medical College, New Delhi, India. DESIGN: Prospective, randomized, interventional, comparative clinical study. METHODS: Twenty-four eyes with mild-to-moderate FECD underwent phacoemulsification with a standardized 4-mm central DWEK and were randomized to DWEK-alone (Group A) or DWEK + topical ripasudil 0.4% four times daily for eight weeks (Group B). Primary outcomes were changes in best-corrected visual acuity (BCVA, logMAR), central corneal thickness (CCT), and peripheral endothelial cell density (ECD) over 6 months. Secondary outcomes were time to corneal clearing, optical quality (higher-order aberrations), and complications. RESULTS: BCVA improved significantly in both groups, with faster recovery in Group B (0.24 ± 0.11 vs. 0.36 ± 0.14 logMAR at 6 months; p < 0.05). CCT decreased more rapidly in Group B (572.1 ± 27.5 µm vs. 590.2 ± 29.8 µm; p = 0.04). Peripheral ECD remained stable in Group A but showed a mild gain in Group B (p = 0.04). Group B achieved earlier corneal deturgescence (mean 8 ± 2 weeks vs. 12 ± 3 weeks). No eye in either group developed persistent edema, interface haze, or required rescue keratoplasty. CONCLUSIONS: Adjunctive topical ripasudil after DWEK accelerates endothelial recovery, corneal deturgescence, and visual rehabilitation compared with DWEK alone, without compromising safety.
25 Apr 2026
Read appraisal →The British journal of ophthalmology
Two-year outcomes of repeated red light therapy in premyopic children: sustained efficacy and rebound effects
BACKGROUND: To evaluate the long-term prevention effectiveness and rebound effect of repeated red light therapy (RRLT) in children with premyopia over 2 years. METHODS: A total of 108 premyopic children (cycloplegia spherical equivalent refraction (SER): -0.50 to +0.75 D) were enrolled and followed for 24 months. Participants were randomly assigned to the RRLT or control groups. The RRLT was administered two times per day for 3 min per session, with at least 4-hour interval. At the beginning of the second year, participants receiving RRLT were further randomised into continued treatment and washout subgroups. Axial length (AL), SER and subfoveal choroidal thickness (SChT) were measured. RESULTS: Over 2 years, the RRLT group showed significantly smaller AL elongation (0.26 mm; 95% CI 0.18 to 0.35 mm) and SER progression (-0.21 D; 95% CI -0.35 to -0.08 D) compared with the controls (AL: 0.43 mm; 95% CI 0.36 to 0.49 mm; SER: -0.66 D; 95% CI -0.79 to -0.52 D). The RRLT group also demonstrated significantly less SChT thinning (-2.44 µm; 95% CI -16.11 to 11.23 µm) than the controls (-44.12 µm; 95% CI -53.05 to -35.19 µm). After RRLT cessation in the second year, the washout subgroup exhibited significantly faster AL elongation and more SChT thinning than the controls, with no significant difference in SER progression. CONCLUSIONS: The 2-year RRLT intervention effectively retarded AL elongation and SER progression in premyopic children by 0.17 mm and -0.45 D, respectively. Notably, a significant rebound effect was observed in AL growth following 1-year RRLT cessation.
25 Apr 2026
Read appraisal →The British journal of ophthalmology
Multimodal artificial intelligence for retinal detachment diagnosis using fundus imaging and patient questionnaires
BACKGROUND/AIMS: This study aimed to develop a multimodal artificial intelligence (AI) system that integrates fundus imaging and patient questionnaire data to achieve clinician-level diagnostic accuracy for diagnosing retinal detachment (RD). METHODS: Ultra-widefield fundus images and comprehensive patient questionnaires were collected from patients with RD and healthy controls at Tsukazaki Hospital. A multimodal model was developed using the Contrastive Language-Image Pretraining framework to classify RD cases, alongside separate image-only and questionnaire-only models for comparison. Per-image and per-subject analyses were conducted to assess model performance. RESULTS: The multimodal model outperformed single-modal models in per-image and per-subject assessments. It achieved accuracy, recall and F1 scores of 0.899±0.054, 0.902±0.043 and 0.902±0.048 in the per-image analysis and 0.893±0.071, 0.949±0.044 and 0.873±0.074 in the per-subject analysis, respectively. The AI model's overall diagnostic accuracy was slightly lower than that of human clinicians; however, it exhibited a higher recall rate, indicating improved detection of true RD cases. CONCLUSION: Integrating fundus imaging with patient questionnaire data significantly improves AI-based RD diagnosis. Future research needs to focus on expanding the dataset and refining the questionnaire design to further improve model performance.
25 Apr 2026
Read appraisal →The British journal of ophthalmology
Incidental ocular surface squamous neoplasia in pterygia: a systematic review and meta-analysis
AIMS: Differentiating pterygium from ocular surface squamous neoplasia (OSSN) is important for guiding management. This meta-analysis evaluates the prevalence and risk factors for incidental OSSN in clinically diagnosed pterygia. METHODS: Ovid Embase, MEDLINE, Cochrane Library and Web of Science were systematically searched from January 2000 to February 2025. Included studies analysed ≥100 clinically diagnosed pterygia via histopathology. Random-effects meta-analysis assessed the prevalence of incidental OSSN among pterygia. Risk factors were evaluated using the Mantel-Haenszel and inverse variance methods, and meta-regression analysed the influence of publication year, geographic proximity to the equator, and country-level ultraviolet (UV) radiation exposure. RESULTS: 12 studies, comprising 8688 specimens, identified 202 cases of incidental OSSN (pooled prevalence=1.32%, 95% CI 0.41% to 4.21%, I2=95.3%). Meta-regression revealed that lower OSSN prevalence was associated with greater distance from the equator (OR 0.49, 95% CI 0.28 to 0.83, p<0.01), while higher prevalence was associated with greater UV exposure (OR 2.20, 95% CI 1.17 to 4.14, p=0.01). Publication year had no effect (p=0.98). Age (p=0.18), sex (p=0.45) and lesion location (p=0.60-0.82) did not differ between incidental OSSN cases and benign pterygia. Incidental OSSN prevalence also did not differ between primary and recurrent pterygia (p=0.23). Regional analyses revealed variation in prevalence: Europe (0.29%), Asia (0.76%), North America (1.03%), Oceania (8.57%) and South America (14.97%). CONCLUSIONS: This meta-analysis, based on low- to very low-certainty evidence, identified a 1.32% pooled prevalence of incidental OSSN in clinically diagnosed pterygia, highlighting the potential influence of UV exposure and equatorial proximity. The overlap in demographic and lesion characteristics between benign pterygia and OSSN underscores diagnostic challenges.
25 Apr 2026
Read appraisal →The Journal of clinical endocrinology and metabolism
Secukinumab in Moderate-to-Severe Graves Orbitopathy: A Randomized, Double-Blind, Placebo-Controlled, Multicenter Study
CONTEXT: Interleukin (IL)-17, a key proinflammatory cytokine, drives inflammation and fibrosis in Graves orbitopathy (GO), and elevated IL-17 and Th17 cells correlate with disease activity and severity. OBJECTIVE: The ORBIT study aimed to evaluate the efficacy and safety of secukinumab, an IL-17A inhibitor, in individuals with active, moderate-to-severe GO. METHODS: A randomized, double-blind, placebo-controlled, parallel-group, multicenter trial was conducted. Adults with active, moderate-to-severe, non-sight-threatening GO randomly (1:1) received secukinumab 300 mg or placebo subcutaneously over a 16-week double-blind treatment period, followed by an additional 16-week open-label treatment phase for proptosis nonresponders. Safety parameters, thyroid-related hormones, and autoantibodies were also assessed. The primary end point was overall response of reduced Clinical Activity Score (CAS) of 2 or more points and a reduction of 2 mm or greater in proptosis from baseline without worsening in the fellow eye at week 16. RESULTS: Twenty-eight adult GO patients with a CAS of 4 or greater were enrolled (secukinumab, n = 14; placebo, n = 14). None in either the secukinumab or placebo group achieved an overall response at weeks 16 and 32, respectively, when all patients received open-label secukinumab. No clinically meaningful changes were observed in ophthalmic symptoms and signs, proptosis, lid aperture, eye muscle motility, CAS, and health-related quality of life either at week 16 or week 32. No meaningful effect on serum levels of thyroid-related hormones and antibodies was observed. Secukinumab was well tolerated, with mostly mild adverse events. Neither treatment-induced study discontinuation nor new safety signals were registered. CONCLUSION: Secukinumab did not show clinical efficacy vs placebo when treating patients with active, moderate-to-severe GO.
24 Apr 2026
Read appraisal →Scientific reports
A hybrid dual-stream CNN framework with dynamic data augmentation and improved Manta Ray Foraging Optimization for robust glaucoma detection.
A progressive neurological condition, glaucoma is one of the main causes of irreversible blindness in the globe. Early detection is crucial to preventing irreversible vision loss however conventional diagnostic methods are often time-consuming, and heavily reliant on clinical expertise. This study presents an innovative deep learning framework designed to automate glaucoma detection while addressing key challenges such as data imbalance, image quality inconsistencies, and the need for accurate feature extraction. The proposed framework begins with a dedicated preprocessing pipeline that enhances image clarity, and isolates clinically relevant regions of interest. To handle class imbalance, a novel hybrid data augmentation strategy is introduced, combining geometric transformations with adaptive Gaussian noise injection that adjusts intensity according to image characteristics, thereby simulating realistic clinical variability. At its core, the framework leverages a dual-stream CNN architecture that integrates DenseNet121 for structural feature extraction and ResNet50 for texture representation. A lightweight channel-wise attention mechanism is then introduced to selectively emphasize clinically significant channels while suppressing redundant features, thereby balancing efficiency and interpretability. To optimize model performance while reducing computational overhead, an Improved Manta Ray Foraging Optimization (IMRFO) algorithm is employed. IMRFO enhances the standard MRFO with Partial Centroid Opposition-Based Learning (PCOBL) to dynamically fine-tune hyperparameters, including augmentation settings and transfer learning configurations. Experimental validation was conducted on four public benchmark datasets, ACRIMA, Drishti-Gs, ORIGA, and RIM-ONE-DL, demonstrating the framework's superior performance across all evaluation metrics. The model achieved 100.00% accuracy, precision, recall, and AUC on both ACRIMA and Drishti-Gs, with losses of 0.003 and 0.001, respectively. On ORIGA, it reached 99.70% accuracy, 99.80% precision, 99.30% recall, and 99.50% AUC (loss = 0.01). On RIM-ONE-DL, the model scored 99.90% accuracy, 99.70% precision, 99.50% recall, and 99.70% AUC (loss = 0.006). These findings confirm the framework's robustness and clinical applicability for effective glaucoma screening.
19 Apr 2026
Read appraisal →Medical science monitor : international medical journal of experimental and clinical research
Use of Deep Learning Models in the Diagnosis of Proptosis Through Orbital Magnetic Resonance Imaging
BACKGROUND Proptosis is a common manifestation of orbital disease; however, current diagnostic tools, such as the Hertel exophthalmometer and manual radiological measurements, have limited reproducibility and are observer-dependent. More objective, automated approaches are needed. In this single-center retrospective study, orbital magnetic resonance imaging (MRI) examinations from 521 participants (261 with proptosis, 260 controls) were analyzed. Proptosis was defined on MRI using interzygomatic line-based distance criteria. Three-dimensional convolutional neural network models based on DenseNet121, DenseNet169, DenseNet264, and ResNet50 architectures were trained on volumetric orbital MRI data. MATERIAL AND METHODS Data were divided into training, validation, and test sets, and 5-fold cross-validation with early stopping was used to optimize and validate model performance. Diagnostic performance was assessed using accuracy, area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. RESULTS DenseNet121 achieved the best overall performance, with mean accuracy of 95.0%, AUC of 0.986, sensitivity of 92.7%, and specificity of 96.9% across 5-fold cross-validation. CONCLUSIONS To the best of our knowledge, prior artificial intelligence studies in orbital imaging have primarily focused on CT-based measurements, radiomics approaches, or thyroid-associated orbitopathy assessment rather than end-to-end 3-dimensional deep learning analysis of orbital MRI volumes. In this context, the present study explores a volumetric MRI-based deep learning framework for automated proptosis detection, emphasizing patient-level classification and explainable model interpretation.
19 Apr 2026
Read appraisal →Journal of neurointerventional surgery
Transverse venous sinus stenting versus cerebrospinal fluid shunting in idiopathic intracranial hypertension: a multi-institutional and multinational database study
BACKGROUND: Cerebrospinal fluid (CSF) shunting and transverse venous sinus (TVS) stenting are promising treatment choices for idiopathic intracranial hypertension (IIH), addressing different aspects of IIH pathophysiology. However, large-scale comparative data remain limited. METHODS: We performed a multinational, multi-institutional, retrospective propensity score-matched analysis using the TriNetX platform. Adult patients diagnosed with IIH who underwent either TVS stenting or CSF shunting were included. The primary outcome was treatment failure, defined as the need for subsequent procedures (repeat TVS stenting, CSF shunt, or optic nerve fenestration). Secondary outcomes included residual symptoms (headache, visual disturbances, dizziness, and pulsatile tinnitus), ongoing use of IIH-related medications (furosemide, acetazolamide, and topiramate), and healthcare utilization, assessed through unplanned hospital readmissions and emergency department (ED) visits at 1 year follow-up. RESULTS: Of 134 530 IIH patients identified, 1362 underwent TVS stenting and 5278 underwent CSF shunting. After propensity score matching, patients who received TVS stenting had significantly lower odds of repeat interventions (9.6% vs 38.6%; OR 0.169, 95% CI 0.136 to 0.212, P<0.001). They also had significantly lower odds of residual headache, visual disturbances, and papilledema. However, stenting was associated with higher odds of persistent pulsatile tinnitus (6.7% vs 2.2%; OR 3.183, P<0.001). TVS stenting was associated with lower odds of unplanned inpatient readmissions (24.1% vs 40.5%; OR 0.466, P<0.001) and ED visits (22.0% vs 34.0%; OR 0.548, P<0.001). CONCLUSION: Both TVS stenting and CSF shunting resulted in symptomatic improvement and reduced medication use in patients with IIH. However, stenting was associated with significantly lower treatment failure rates, fewer residual papilledema, headaches and visual symptoms, and reduced healthcare utilization. Persistent pulsatile tinnitus was more common following stenting.
17 Apr 2026
Read appraisal →European journal of pediatrics
The impact of growth hormone therapy on ocular structural parameters in idiopathic short stature: a systematic review and meta-analysis
This study aimed to conduct a systematic review and meta-analysis of the effects of growth hormone (GH) therapy on ocular structures and biometric parameters in children with idiopathic short stature (ISS), synthesizing existing evidence for future research. We systematically searched PubMed, EMBASE, the Cochrane Library, CNKI, Wanfang, and VIP databases from inception to September 2025. Randomized controlled trials and observational studies comparing the effects of GH therapy versus control on ocular biometric and refractive outcomes in children with ISS were included. Study quality was assessed using the Cochrane Risk of Bias Tool (RoB 2) and the NOS. Meta-analysis was performed using R software version 4.3.2, with effect sizes expressed as MD or SMD and 95% CI. Nine studies were included (4 RCTs, 5 cohort studies). Meta-analysis showed no significant association of GH therapy with corneal curvature (SMD = 0.13, 95% CI: -0.34 to 0.61). For axial length, the random-effects model showed no significant difference (MD = 0.72, 95% CI: -0.36 to 1.81), with high heterogeneity (I2 = 91.7%). Similarly, no significant association was found for intraocular pressure (MD = 1.38, 95% CI: -0.66 to 3.43; I2 = 95.9%). However, a significant association was observed for refractive status (MD = 0.53, 95% CI: 0.20 to 0.86; I2 = 0.0%). Sensitivity analysis showed robust results for corneal curvature and refractive outcomes, while axial length and intraocular pressure results were influenced by individual studies. Funnel plots and Egger's test indicated no significant publication bias.Conclusion: The associations between GH therapy and ocular biometric parameters in children with ISS remain uncertain. Current evidence suggests a possible link with refractive changes but shows no significant associations with axial length or intraocular pressure when accounting for high heterogeneity. The findings are inconsistent and should be regarded as hypothesis-generating, insufficient to recommend changes in clinical monitoring practice. Future large-sample, long-term studies are required.
17 Apr 2026
Read appraisal →Science advances
Enhancing biomedical optical volumetric imaging via self-supervised orthogonal learning
Optical volumetric imaging grapples with inherent noise problems arising from photon budget constraints, light scattering, and space-bandwidth product bottlenecks, all of which degrade structural fidelity and become even more pronounced than in planar imaging. While deep learning presents potential for denoising, supervised methods are hindered by the impracticality of paired datasets, and existing self-supervised approaches fail to fully exploit the intrinsic volumetric structural redundancy inherent to optical imaging. Here, we present a self-supervised volumetric biomedical imaging denoiser (VALID) that leverages intrinsic three-dimensional spatial coherence for highly efficient volumetric denoising through a self-supervised orthogonal learning framework. VALID demonstrates robust denoising performance across diverse imaging modalities, including two- and three-photon microscopy, light-field microscopy, and optical coherence tomography, substantially enhancing structural fidelity in deep-tissue, multimodal, and dynamic imaging scenarios. By combining computational efficiency with zero-shot adaptability, VALID establishes a transformative approach to volumetric image enhancement with structure-aware precision.
16 Apr 2026
Read appraisal →Modern rheumatology
Epidemiological and clinical insights into Behçet's disease in Japan: a 2019 nationwide analysis
OBJECTIVES: To clarify contemporary epidemiological and clinical trends of Behçet's disease in Japan by analysing the 2019 National Database of Designated Intractable Diseases (MHLW), describing patient demographics, organ involvement, impact on daily life, and treatment patterns. METHODS: This study analysed the 2019 data from the National Database of Designated Intractable Diseases of Japan, provided by the Japanese Ministry of Health, Labour and Welfare. We focused on age distribution, symptom frequency, daily life impact, and treatment trends. RESULTS: In the 2019 database, 10 406 patients with BD were registered. The median age was 55 years, with a higher proportion of women affected (53.2%). Serious manifestations such as ocular, vascular, and central nervous system involvement were more frequent in males, whereas females showed more mucocutaneous and articular symptoms, representing mild forms of the disease. Pharmacological treatment was common (93.8%), with corticosteroids (44.3%), colchicine (50.9%), infliximab (17.0%), and adalimumab (9.6%) being frequently used. BD significantly affected daily life, with nearly one-third experiencing impaired mobility or depression. CONCLUSIONS: This nationwide analysis highlighted sex-based differences in clinical features and found that BD continues to substantially affect quality of life. The widespread use of biologics suggests a shift in therapeutic strategies.
16 Apr 2026
Read appraisal →Disaster medicine and public health preparedness
The Beirut Pager Explosions of 2024: A Systematic Review of an Unprecedented Mass Casualty Event
OBJECTIVE: In September 2024, Lebanon experienced an unprecedented mass casualty incident involving the simultaneous detonation of thousands of weaponized pager devices. This systematic review characterizes the resulting injury patterns, surgical burden, and disaster-response lessons to help inform future preparedness and response strategies. METHODS: PubMed, Embase, Scopus, and Web of Science were searched for studies on casualties from the pager explosions. A random-effects meta-analysis estimated pooled prevalence of injuries by body region. RESULTS: Sixteen articles met inclusion criteria overall, comprising seven studies in the clinical synthesis and nine articles in the qualitative disaster-response synthesis. A consistent injury pattern predominantly affected the hands, face, and eyes. Specialty-specific cohorts demonstrated severe ocular destruction, frequent hand amputation, and a substantial operative burden. In the two comparable hospital-based cohorts eligible for meta-analysis, upper extremity injuries were most common (pooled prevalence 84%; 95% CI: 73-91%; I2 = 0%). The incident exposed critical weaknesses in triage, communication, and specialty surge capacity. CONCLUSION: The 2024 pager explosions produced a novel injury signature distinct from conventional blast trauma. These findings, while based on limited case series, underscore the urgent need for health care systems to develop flexible, adaptive disaster plans that can respond to the unique threats of unconventional warfare.
14 Apr 2026
Read appraisal →PloS one
MaxGRNet: A multi-axis vision transformer with improved generalization for eye disease classification using explainable AI with insertion-deletion operations on fundus images
Eye diseases, including diabetic retinopathy (DR), glaucoma, and cataracts, represent a major global health concern and can lead to severe visual impairment or blindness if not identified in a timely manner. This study proposes a novel eye disease classification framework based on a multi-axis vision transformer (MaxViT) applied to color fundus images with Explainable Artificial Intelligence (XAI) techniques to enhance model transparency. The proposed architecture integrates transformer-based attention mechanisms with Global Response Normalization (GRN)-based multi-layer perceptron (MLP) layers to capture complex spatial and contextual relationships within fundus images effectively. The model was evaluated on a publicly available eye disease classification dataset using a five-fold cross-validation strategy to assess its robustness and generalization. The experimental results show that the proposed approach consistently outperforms conventional Convolutional Neural Networks (CNNs) and Vision Transformer (ViT) variants, including ResNet50, Swin-T, MaxViT-T, and ViT-B16. The model achieved a macro-averaged test accuracy, precision, and recall values of 96.75%, 96.70%, and 96.80%, respectively, with paired statistical t-tests confirming that these improvements were statistically significant. Rigorous preprocessing techniques were employed to improve data consistency, and XAI-based visual explanations provided insights into the model's decision-making process, supporting interpretability in ophthalmic image analysis. Overall, the proposed MaxViT-based framework is robust and computationally feasible for research-oriented evaluation approaches for automated fundus image classification, highlighting the potential of advanced transformer architectures for future decision-support and research-oriented ophthalmic applications.
10 Apr 2026
Read appraisal →Translational vision science & technology
OptiDON-Guard: An Ensemble Model for Identification of Dysthyroid Optic Neuropathy Using Optical Coherence Tomography Angiography
PURPOSE: To explore the value of machine learning models based on optical coherence tomography angiography (OCTA) in identifying dysthyroid optic neuropathy (DON). METHODS: A retrospective, dual-center, cross-sectional study was conducted involving 346 thyroid eye disease patients (with/without DON) from two institutions. Participants underwent ophthalmic examinations, including OCTA scans centered on the macula and optic nerve head. The OCTA data from Center 1 were split into a training set and an internal validation set, while the data from Center 2 served as an external test set. Features were extracted from OCTA images, and a wrapper-based feature selection strategy identified the optimal subset. A two-tiered stacking modeling framework was used, incorporating baseline model construction, data augmentation using pseudo-labeling, and ensemble integration. Model performance (area under the curve [AUC], accuracy, sensitivity, specificity, precision) was evaluated alongside receiver operating characteristic, calibration, and decision curve analyses. Additionally, the SHapley Additive exPlanation method was used to interpret feature contributions. RESULTS: The ensemble model, OptiDON-Guard, demonstrated superior performance to individual models on both internal validation and external test sets. In the internal validation set, it achieved an AUC of 0.872, accuracy of 0.943, sensitivity of 0.667, specificity of 0.979, and precision of 0.943. In the external test set, it maintained strong performance with an AUC of 0.854, accuracy of 0.816, sensitivity of 0.767, specificity of 0.842, and precision of 0.854. CONCLUSIONS: The OptiDON-Guard is a reliable, interpretable, and clinically applicable tool for identifying DON. TRANSLATIONAL RELEVANCE: The OptiDON-Guard offers a valuable clinical tool to enhance the diagnostic precision of DON.
9 Apr 2026
Read appraisal →JMIR research protocols
Remote Management of Patients With Diabetic Macular Edema Receiving Long-Term Intravitreal Anti-Vascular Endothelial Growth Factor Therapy (RE-SHINE Study): Protocol for a Real-World Study
BACKGROUND: Long-term intravitreal anti-vascular endothelial growth factor therapy is the gold standard for treating diabetic macular edema (DME). However, poor treatment adherence in real-world settings often leads to suboptimal visual outcomes. Evidence-based remote management strategies are required to bridge the gap between clinical protocols and patient self-management. OBJECTIVE: This study aims to describe a remote management program based on the Behavior Change Wheel model designed to increase treatment adherence and improve disease outcomes for patients with DME receiving long-term anti-vascular endothelial growth factor therapy, and to report the study protocol. METHODS: The RE-SHINE study was structured into three phases: theoretical modeling, digital platform development, and a validation cohort study. The intervention included a smartphone-based app providing personalized health education, self-monitoring of vision (Amsler grid), injection reminders, and direct communication with health care providers. The efficacy of the remote management model was tested in a real-world cohort study. Participants were assessed at baseline and at each follow-up visit over 12 months. The primary outcome measure was best-corrected visual acuity, and secondary outcome measures included central retinal thickness and treatment adherence (injection rate and follow-up frequency). RESULTS: A total of 1006 patients with DME underwent initial screening, and 958 (95.2%) patients met the inclusion criteria, with only 7 patients declining follow-up. We anticipate that the remote management system will be both feasible and acceptable for patients with DME. It is hypothesized that the intervention group would demonstrate significantly higher levels of treatment adherence and better visual preservation compared to the retrospective control group. In addition, we intend to assess the impact of the digital intervention on patient-reported outcomes, encompassing quality of life and self-efficacy. Participant recruitment began in September 2023 and was completed by the time of submission. The data analysis is yet to begin. The results are expected to be published in 2026. CONCLUSIONS: A Behavior Change Wheel-based remote management approach could be a scalable and sustainable method to enhance adherence in patients requiring long-term intravitreal therapy. If proven effective, this model could be integrated into routine ophthalmic care to mitigate the risk of vision loss due to undertreatment in DME.
7 Apr 2026
Read appraisal →Translational vision science & technology
Diagnosis of High Intracranial Pressure by Non-Optic Nerve Retinal Image Features
PURPOSE: To assess the performance of an artificial intelligence deep learning (DL) model compared with neuro-ophthalmologists for the classification of subjects with elevated intracranial pressure (ICP) and papilledema versus control subjects, based on retinal images with and without masking the optic nerve heads (ONHs). METHODS: Widefield retinal images were obtained in 32 subjects (70 images) with elevated ICP and papilledema and 31 control subjects (62 images). ONH-unmasked images were generated by cropping the image to a 30° circular region. ONH-masked images were generated by masking the ONH and peripapillary region. Classification was performed using a convolutional neural network model and by two neuro-ophthalmologists (graders). RESULTS: For the ONH-unmasked images, the classification accuracies of the model, grader 1, and grader 2 were 83% (area under the receiver operating characteristic curve [AUC] = 0.94), 93%, and 86%, respectively. For the ONH-masked images, the classification accuracies of the model, grader 1, and grader 2 were 79% (AUC = 0.90), 66%, and 76%, respectively. The classification performance of the DL model and graders did not significantly differ for both datasets (P ≥ 0.38). There was no significant effect of ONH masking on the performance of the DL model (P = 1.0) and grader 2 (P = 0.38), whereas the performance of grader 1 was significantly reduced (P = 0.02). CONCLUSIONS: Both expert graders and the DL models demonstrated excellent performance for classifying retinal images of subjects with elevated and normal ICP using images with or without masking the ONH. TRANSLATIONAL RELEVANCE: Methods for assessment of non-optic nerve retinal image features have the potential to improve diagnosis and monitoring the progression and response to treatment of elevated ICP.
7 Apr 2026
Read appraisal →International ophthalmology
Retinal image-based cardiovascular risk prediction using AI-CRS: a multi-modal deep learning framework
This study presents AI-CRS, an AI-driven deep learning framework for cardiovascular risk assessment using retinal images. By combining convolutional neural networks (CNNs), attention mechanisms, and vasculature segmentation, AI-CRS overcomes the limitations of traditional methods, such as reliance on handcrafted features and single-modality data. The model integrates multi-modal information, fusing vasculature segmentation maps with raw image data to capture subtle vascular changes indicative of cardiovascular diseases. Experimental validation demonstrates that AI-CRS outperforms conventional diagnostic techniques in cardiovascular risk stratification, achieving superior sensitivity, specificity, and accuracy. The model excels in detecting early-stage disease and subtle vascular anomalies, and it shows strong generalizability across diverse datasets, patient demographics, and varying image qualities. Beyond cardiovascular risk, AI-CRS also shows promise in assessing systemic health conditions like hypertension and diabetes, demonstrating the broader utility of retinal imaging in health monitoring. The attention-driven architecture enhances model interpretability, providing clinicians with visual explanations of disease-associated vascular patterns, which is essential for clinical adoption. AI-CRS offers a non-invasive, scalable solution that can be integrated into routine clinical practice, supporting early diagnosis, personalized care, and population-wide screening. Its automated analysis reduces the need for manual feature extraction and subjective interpretation, streamlining workflows and improving efficiency. Ultimately, AI-CRS represents a significant step toward precision medicine by enabling timely interventions, reducing healthcare costs, and improving patient outcomes through non-invasive digital biomarkers.
7 Apr 2026
Read appraisal →PloS one
Vision Transformer attention alignment with human visual perception in aesthetic object evaluation
Visual attention mechanisms play a crucial role in human perception and aesthetic evaluation. Recent advances in Vision Transformers (ViTs) have demonstrated remarkable capabilities in computer vision tasks, yet their alignment with human visual attention patterns remains underexplored, particularly in aesthetic contexts. This study investigates the correlation between human visual attention and ViT attention mechanisms when evaluating handcrafted objects. We conducted an eye-tracking experiment with 30 participants (9 female, 21 male, mean age 24.6 years) who viewed 20 artisanal objects comprising basketry bags and ginger jars. Using a Pupil Labs eye-tracker, we recorded gaze patterns and generated heatmaps representing human visual attention. Simultaneously, we analyzed the same objects using a pre-trained ViT model with DINO (Self-DIstillation with NO Labels), extracting attention maps from each of the 12 attention heads. We compared human and ViT attention distributions using four complementary metrics-Kullback-Leibler divergence, Structural Similarity Index (SSIM), Pearson's Correlation Coefficient (CC), and Similarity (SIM)-across varying Gaussian parameters ([Formula: see text]), yielding 1,152,000 distance evaluations. Additionally, we performed Areas of Interest (AOI) analysis to quantify ViT attention concentration within object regions. Statistical analysis revealed optimal correlation at [Formula: see text], with attention head #12 showing the strongest alignment with human visual patterns across all metrics. Significant differences were found between attention heads, with heads #7 and #9 demonstrating the greatest divergence from human attention ([Formula: see text]), Tukey HSD test). AOI analysis confirmed that all ViT heads concentrated attention significantly more within object regions than background areas ([Formula: see text]), with heads #12, #1, and #3 achieving lift values of +30 to +40 percentage points. Results indicate that while ViTs exhibit more global attention patterns compared to human focal attention, certain attention heads can approximate human visual behavior, particularly for specific object features like buckles in basketry items. These findings suggest potential applications of ViT attention mechanisms in product design and aesthetic evaluation, while highlighting fundamental differences in attention strategies between human perception and current AI models.
6 Apr 2026
Read appraisal →PloS one
Safety and efficacy of Vectored thermal pulsation, Intense pulsed light, and Eyelid Warm compress therapies (VIEW) for meibomian gland dysfunction: Study design and baseline characteristics of a randomized controlled trial
BACKGROUND: Meibomian gland dysfunction (MGD) is one of the most prevalent chronic eye diseases, characterized by meibomian gland obstruction and abnormal meibum secretion both in quantity and quality. OBJECTIVE: To assess the efficacy and safety of vectored thermal pulsation (VTP), intense pulsed light plus meibomian gland expression (IPL + MGX), and eyelid warm compress (EW). METHODS: This is a 3-arm, assessor-masked, randomized controlled trial. 360 symptomatic mild to moderate MGD subjects would be recruited and randomized into 3 intervention groups. Subjects from Group-A, Group-B, and Group-C would be treated with one-session VTP (at baseline), four-session IPL + MGX (at baseline, month-1, month-2, and month-3 visits), and twice-daily EW for 15 months, respectively. All subjects would be prescribed topical artificial tears (Hypromellose, 0.3% w/v) to be used as frequently as needed from wash-out (1 month before baseline) to endpoint (month-15 visit). Subjects would be assessed 9 times throughout a total of 16-month study period. Five self-administered questionnaires would be collected each time in random order. Masked observers would conduct examinations using slit-lamp biomicroscopy, LipiView® II, and Keratograph 5M machines. The eye with worse MGD would be selected and analyzed as the study eye. Tear film break-up time (TFBUT) at month-6 and month-15 visits are the primary outcomes. RESULTS: 374 subjects were enrolled and randomized into three groups, and 372 subjects (aged 54.4 ± 14.7 years) were finally analyzed. The mean of TFBUT time at baseline was 3.1 ± 1.7 seconds. CONCLUSION: This is the first head-to-head comparison of three physical treatments for mild to moderate MGD on their therapeutic efficacy, safety, longevity and compliance in a randomized controlled trial setting.
5 Apr 2026
Read appraisal →Blood
Belantamab mafodotin, lenalidomide, and dexamethasone for intermediate-fit and frail patients with newly diagnosed myeloma.
The phase 1/2 BelaRd (belantamab mafodotin [belamaf], lenalidomide, and dexamethasone) study evaluated the efficacy and safety of belamaf combined with lenalidomide and dexamethasone in unfit and frail transplant-ineligible patients with newly diagnosed multiple myeloma (NDMM). Part 1 (n = 36) established a recommended belamaf phase 2 dose (RP2D) of 1.9 mg/kg every 8 weeks (median follow-up, 39.3 months). In part 2, 30 patients were randomized 1:1 in group A (n = 15), where belamaf dosing was guided by ophthalmologist-assessed ocular adverse events (OAEs), whereas in group B (n = 15), belamaf dosing was based on hematologist-led vision-related anamnestic (VRA) tool and ophthalmologist-assessed grade ≥3 OAEs. Among the RP2D patients (n = 42), overall response rate was 97.6%, median progression-free survival (PFS)/overall survival have not been reached yet, and the 18-month PFS and time to progression rates were 83.0% and 97.2%, respectively. Ocular toxicities were similar between assessments by hematologists and ophthalmologists, and no ophthalmologist withholding of a hematologist-led dosing occurred. Less than 1% of patients stopped driving/reading because of OAEs. Median time to belamaf reinfusion was 13 weeks. Overall, BelaRd is an effective regimen for transplant-ineligible patients with NDMM and warrants a phase 3 study in this setting. OAEs' impact on quality of life appears limited, and implementation of the hematologist-led VRA tool may eventually reduce the necessity for ophthalmologist assessments. This trial was registered at www.clinicaltrials.gov as #NCT04808037.
5 Apr 2026
Read appraisal →Journal of global health
Exposure-response association between outdoor activity time and myopia risk in Chinese children and adolescents: a systematic review and meta-analysis
BACKGROUND: The prevalence of myopia among Chinese children and adolescents has increased sharply over recent decades, raising concern about modifiable environmental risk factors. Although outdoor activity is widely recognised as protective, the quantitative exposure-response relationship within the Chinese educational context remains incompletely defined. METHODS: A systematic review and meta-analysis were conducted following PRISMA guidelines. Studies examining the association between daily outdoor activity time and myopia risk among Chinese youth were identified from major English and Chinese databases (Web of Science, PubMed, CNKI, and Wanfang) through October 2025. Random-effects meta-analysis was conducted to pool odds ratios (ORs). Prespecified subgroup and sensitivity analyses examined exposure thresholds, study design, and geographic latitude. RESULTS: We included 31 studies comprising 380 215 participants. Greater outdoor activity was consistently associated with lower myopia risk (pooled OR = 0.75; 95% confidence interval (CI) = 0.71-0.80), although substantial heterogeneity was observed (I2 = 94%). Compared with <1 hour per day, outdoor exposure of 1-2 hours (OR = 0.85; 95% CI = 0.79-0.92), 2-3 hours (OR = 0.86; 95% CI = 0.78-0.95), and > 3 hours (OR = 0.74; 95% CI = 0.63-0.87) was associated with reduced risk. When dichotomised at two hours, exposure >2 hours yielded an OR of 0.74 (95% CI = 0.69-0.80). Findings were directionally consistent across study designs and latitude strata. CONCLUSIONS: Greater outdoor activity time is associated with lower myopia risk among Chinese children and adolescents. An approximate daily exposure of around two hours appears to represent a pragmatic benchmark under current educational conditions.
4 Apr 2026
Read appraisal →Diagnostic and interventional imaging
Deep learning-based image reconstruction significantly improves image quality of MRI examinations of the orbit at 3 Tesla.
PURPOSE: The purpose of this study was to assess the benefit of a deep learning-based image reconstruction (DLBIR) for improving image quality in orbital magnetic resonance imaging (MRI) at 3 Tesla (T). MATERIALS AND METHODS: Seventy-one patients (48 women and 23 men) with a mean age of 52 ± 19.5 (standard deviation [SD]) years (age range: 7-90 years) who underwent MRI examination of the orbit at 3 T between January and June of 2024, were included in the study. Coronal T2-weighted MR images obtained in 70 patients and post-contrast fat-saturated (FS) coronal T1-weighted MR images obtained in 25 patients, were reconstructed with and without DLBIR, resulting in four imaging sets. Two radiologists independently and blindly measured the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of the optic nerves on the four imaging sets. Image quality and orbital abnormalities were assessed using a standardized 5-point Likert scale. Comparisons between MR images obtained with and without DLBIR were performed using Wilcoxon test for ordinal and quantitative variables and McNemar test for paired binary data. RESULTS: SNR and CNR of coronal T2-weighted MR images were significantly greater using DLBIR (26.67 ± 9.03 [SD], and 14.87 ± 10.31 [SD], respectively) than without DLBIR (18.91 ± 7.28 [SD], and 9.78 ± 8.47 [SD], respectively) (P < 0.001). There were no differences in SNR and CNR between post-contrast FS T1-weighted images obtained with DLBIR (85.56 ± 63.13 [SD], and 64 ± 41.38 [SD], respectively) and those obtained without DLBIR (91.36 ± 48.49 [SD], and 43.25 ± 20.4 [SD], respectively) (P = 0.35, and P = 0.14, respectively). Qualitatively, good-to-excellent image quality was obtained more frequently with DLBIR than without DLBIR for T2-weighted and post-contrast FS T1-weighted images with respect to optic nerve sharpness (67 % vs. 16 %, and 8 % vs. 0 %, respectively), brain sharpness (90 % vs. 6 %, and 68 % vs. 4 %, respectively), and overall image quality (73 % vs. 1 % and 36 % vs. 0 %, respectively) (all P ≤ 0.001). No significant differences in the detection rates of orbital abnormalities were found between MR images obtained with and without DLBIR, including optic nerve hyperintensity (34 % vs. 31 %, respectively; P = 0.16) and optic nerve atrophy (33 % for both) on T2-weighted images, and optic nerve enhancement on post-contrast FS T1-weighted images (16 % for both). CONCLUSION: DLBIR significantly improves image quality of MRI examinations of the orbit at 3 T, without losing clinically relevant information.
4 Apr 2026
Read appraisal →Clinical & experimental optometry
Advancements in artificial intelligence for meibography: clinical utility and contributions to meibomian gland dysfunction management
Recent advances in artificial intelligence (AI) have enhanced the capabilities of meibography by enabling objective and quantitative assessment of meibomian gland structure. This review explores the clinical utility of AI-based meibography in the diagnosis and management of Meibomian Gland Dysfunction (MGD), with a focus on segmentation, morphological analysis, and disease staging. Developments in deep learning have enabled more precise gland feature extraction, including gland dropout, density, and tortuosity, supporting efforts towards standardised and reproducible clinical evaluation. Although not the focus of this review, insights from traditional image processing techniques are referenced to highlight potential areas of improvement in current AI models. Key issues such as limited modelling of regional gland variation, restricted dataset diversity, and lack of standardised image quality control are discussed. Although significant progress has been made, further work is needed to ensure AI-driven meibography tools are generalisable, interpretable, and suitable for broad clinical implementation.
4 Apr 2026
Read appraisal →Ophthalmology
Special AAO Report: Recommendations on Screening for Hydroxychloroquine Retinopathy (2025 Revision)
BACKGROUND: The American Academy of Ophthalmology recommendations on screening for hydroxychloroquine (HCQ) retinopathy are now a decade old. This revision provides an update based on current knowledge. Major points and changes are as follows. NATURE OF TOXICITY: The retina typically remains clinically normal for many years on HCQ, but there is an unpredictable point at which signs of toxicity may develop. PATTERN OF TOXICITY: Most eyes of patients of European heritage have a parafoveal pattern of toxicity, whereas eyes of East Asian patients tend to present with pericentral toxicity, but there are many exceptions, and screening examinations should look for both patterns. DOSAGE: The recommended daily dosage remains unchanged at ≤5.0 mg/day/kg real weight. Keep under 400 mg/day for severely obese patients. RISK FACTORS: Beyond dose and duration, concurrent renal disease and tamoxifen use increase risk, as does initiation of HCQ therapy at an older age. CLINICAL TESTS: The primary screening tool should be OCT, along with wide-pattern fundus autofluorescence (FAF). Visual field (VF) and multifocal electroretinography (mfERG) are secondary confirmatory tests. Newer VF algorithms such as 24-2C can screen parafoveal and pericentral regions simultaneously. SCREENING: A baseline screening examination, including fundus, OCT, and FAF, is advised soon after HCQ is begun, not only to rule out preexisting conditions but also for comparison with later results. Annual screening with OCT and FAF is recommended while using HCQ, but may be deferred during the first 5 years if there are no significant risk factors. PROGRESSION: Mild retinopathy is unlikely to progress after discontinuation of HCQ, but severe retinopathy may continue to worsen for many years. IMPLICATIONS OF EARLY SIGNS OF TOXICITY: With earlier recognition comes greater need to communicate with patients and their medical providers regarding options to stop HCQ or continue with cautious monitoring. NEW TECHNOLOGY: There have been experimental advances in imaging and artificial intelligence analysis for HCQ screening, but none have been validated for general use or are widely available at this time. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found after the references.
1 Apr 2026
Read appraisal →Surv Ophthalmol
Relentless placoid chorioretinitis
Relentless placoid chorioretinitis (RPC) is a rare, vision-threatening posterior uveitis that predominantly affects young adults. The hallmark clinical findings are numerous scattered placoid chorioretinal lesions involving the midperipheral and far peripheral fundus as well as the posterior pole and a persistent or recurrent course resulting in lesions at different chronological stages, with fresh creamy placoid lesions and healing pigmented lesions being present at the same time. Although RPC is frequently described as a disease that is intermediate between acute posterior multifocal placoid pigment epitheliopathy (APMPPE) and serpiginous choroiditis, it more closely resembles APMPPE. It is important to identify those patients with RPC who present with an APMPPE phenotype so that appropriate immunomodulatory therapy is instituted without delay, as most cases of RPC are refractory to corticosteroid monotherapy. Examination findings may help differentiate RPC and APMPPE. Multimodal imaging, including ultra-widefield imaging, and selective investigations aid in distinguishing RPC from other placoid diseases and types of posterior uveitis.
1 Jan 2026
Read appraisal →Nat Commun
Proteome atlas for mechanistic discovery and risk prediction of diabetic retinopathy
Proteomics offers an unprecedented opportunity to characterize and predict diabetic retinopathy (DR) with minimal invasiveness. Here we examine this in 10,873 individuals with (pre)diabetes from two ethnically distinct cohorts. By simultaneous profiling of ~3000 proteins, we identify 668 associations with mechanistically plausible directionality that constitute a comprehensive DR proteomic landscape with linkages to retinal tomographic structure and genetic predisposition, pointing to established and novel biological pathways conferring DR risk. Integrating DR proteomic profile markedly improves predictive performance beyond clinical and genetic predictors, with plexin B2, growth differentiation factor 15, and renin emerging as top proteins validated across cohorts and linked to retinal microvascular degeneration in Guangzhou Diabetic Eye Study (GDES) based on SS-OCTA. A parsimonious panel of these three proteins alone achieves comparable performance in predicting DR development and progression, while renin is confirmed as a causal promoter through genetic analyses. Our findings highlight the potential of large-scale proteomics in elucidating DR pathogenesis and advancing biomarker discovery, with broad implications for early detection and intervention.
1 Oct 2025
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