Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 3 appraisals
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 →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 →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
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