Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 23 appraisals
Proceedings of the National Academy of Sciences of the United States of America
Advancing AI negotiations: A large-scale autonomous negotiation competition.
We conducted an international AI negotiation competition in which participants designed and refined prompts for AI negotiation agents. We then facilitated over 180,000 negotiations between these agents across multiple scenarios with diverse characteristics and objectives. Our findings revealed that principles from human negotiation theory remain crucial even in AI-AI contexts. Surprisingly, warmth-a traditionally human relationship-building trait-was consistently associated with superior outcomes across all key performance metrics. Dominant agents, meanwhile, were especially effective at claiming value. Our analysis also revealed unique dynamics in AI-AI negotiations not fully explained by negotiation theory, including AI-specific technical strategies like chain-of-thought reasoning and prompt injection. When we applied natural language processing methods to the full transcripts of all negotiations, we found positivity, gratitude, and question-asking (associated with warmth) were strongly associated with reaching deals as well as objective and subjective value, whereas conversation lengths (associated with dominance) were strongly associated with impasses. The results suggest the need to establish a new theory of AI negotiation, which integrates classic negotiation theory with AI-specific negotiation theories to better understand autonomous negotiations and optimize agent performance.
7 June 2026
Read appraisal →Open heart
Impact of three-dimensional heart models on surgical planning and outcomes in congenital heart disease: a systematic review and meta-analysis
BACKGROUND: Congenital heart disease surgery includes intricate structures that are perhaps hard to completely understand by traditional two-dimensional shapes. Individualised, centre-based three-dimensional (3D) heart models may enhance structural imaging and surgical preparation, while data on their medical effect continue to be unpredictable and uneven. This systematic review and meta-analysis assessed the impact of 3D heart simulations on surgical preparation and outcomes in congenital cardiac disease. OBJECTIVE: To assess the successful outcomes of patient-centred 3D cardiac simulations in medical preparation, intraoperative execution and surgical results in patients experiencing operation for congenital cardiac disease. OBJECTIVE: To evaluate the impact of patient-specific 3D heart models on surgical planning precision and preoperative outcomes in congenital heart disease surgery. METHODS: A systematic review and meta-analysis were performed in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 standards and prospectively registered in PROSPERO (CRD42024211985). PubMed, Embase, Scopus and Cochrane CENTRAL were searched through March 2025 for comparative studies evaluating 3D-printed or virtual heart models versus conventional imaging. Outcomes were harmonised and pooled as risk ratios (RRs), mean differences (MDs) or standardised MDs (SMDs) with 95% CIs using random-effects models. RESULTS: 32 studies (n=1842) met inclusion criteria. The use of 3D models was associated with more frequent surgical plan modification (RR 1.42; 95% CI 1.21 to 1.67), reduced operative time (MD -18.4 min; 95% CI -27.6 to -9.2), lower postoperative complications (RR 0.74; 95% CI 0.56 to 0.98) and reduced re-operation (RR 0.52; 95% CI 0.31 to 0.86). Hospital stay was shortened (MD: 1.8 days; 95% CI -3.0 to -0.6), and surgeon confidence improved (SMD 0.88; 95% CI 0.60 to 1.15). The confidence of indication extended from modest to small because of non-experimental research and variability. CONCLUSION: Individualised-centred 3D heart stimulation improves structural imaging and medical preparation in congenital cardiac disease, chiefly for intricate lacerations and can increase preoperative competence, while statistics for conclusive diagnosis results continue to be restricted and mainly experiential. Upcoming potential, large-scale investigator-led research with consistent result events are required to ratify their medical success.
26 May 2026
Read appraisal →Studies in health technology and informatics
Digital Health Tool Usability and Redesign: Preliminary Scoping Review Findings
Few usability or user experience studies report how insights resulted in improved digital health tool (DHT) design. Therefore, this paper reports on preliminary findings of a scoping review that examines this topic. A systematic search in five databases led to the inclusion of 102 studies that met the inclusion criteria, namely those reporting both usability evaluation and redesign outcomes. Categorisation of these studies identified four user groups: patients, healthcare professionals, public health users, and mixed groups. Many DTHs were web- (36%) or mobile-based (32%) applications, and most often newly developed (68%).
24 May 2026
Read appraisal →Neurosurgical review
The impact of augmented reality on radiation exposure during spine surgery: a systematic review
BACKGROUND: Augmented reality (AR) has rapidly emerged as an important navigational technology in spine surgery, with growing interest in its potential to reduce dependence on fluoroscopy and mitigate radiation exposure for both patients and operating room staff. METHODS: This systematic review examined current evidence on radiation-related outcomes of AR-assisted spine procedures. A PRISMA-guided search of PubMed, Embase, and Scopus identified twelve eligible studies, including randomized trials, prospective and retrospective clinical cohorts, and cadaveric or synthetic-model investigations. RESULTS: Across the included studies, AR was consistently associated with reduced fluoroscopy use, with several studies demonstrating statistically significant reductions in exposure time compared with conventional fluoroscopy. Two clinical studies directly measured occupational radiation and reported substantially lower staff doses when AR-based navigation and optimized shielding strategies were employed. Patient radiation exposure was similarly decreased in most studies, particularly when AR was integrated with low-dose cone-beam CT protocols. Operative time findings were mixed, reflecting early learning curves and variability in AR systems, but accuracy remained high across platforms. CONCLUSION: Current evidence suggests that AR may reduce intraoperative radiation exposure without compromising workflow or surgical precision, available studies are limited by small sample sizes, heterogenous methodologies, and a paucity of direct staff-dosimetry data. Larger, high-quality multicenter studies are needed to clarify the magnitude of AR's radiation-sparing benefits and to define its role in modern spine surgery.
19 May 2026
Read appraisal →Talanta
SERS technology helps bacterial inactivation in vivo: including detection, identification, and evaluation.
The problem of bacterial infection and antimicrobial resistance has become a major challenge threatening to human health. Detection, identification, and real-time evaluation of bacteria during the sterilization process are of great significance for interpreting the sterilization mechanism and ensuring the effectiveness of sterilization. With the advantages of high sensitivity, fast detection speed, and real-time monitoring, Surface-enhanced Raman Scattering (SERS) technology provides a revolutionary solution to in vivo bacterial detection, identification, and antibacterial effect evaluation through functional substrate optimization, microfluidic chip integration, and deep collaboration of machine learning algorithms. This review focuses on the prominent applications of SERS technology in chemical, biological, and physical sterilization processes. By analyzing the changes in bacterial SERS fingerprint patterns, precise detection and identification of sensitive/resistant bacteria have been achieved, deepening the interpretation of drug resistance mechanisms at the gene, cell, and biofilm levels. Meanwhile, SERS technology provides an ideal tool for monitoring and evaluating the biological sterilization process. In the process of physical sterilization, enhancing the substrate material not only helps to improve SERS signals, but also promotes bacterial inactivation. By monitoring real-time changes in bacterial metabolism, the sterilization process can be precisely regulated. This review summarizes the innovative application of SERS technology in accurately guiding the sterilization process in vivo, providing important support for optimizing antibacterial treatment strategies.
18 May 2026
Read appraisal →BMC anesthesiology
Automated end-tidal control versus manual regulation in low-flow volatile anesthesia: a systematic review and meta-analysis
BACKGROUND: In low-flow anesthesia, automated monitoring of end-tidal oxygen (etO2) and end-tidal anesthetic agents (etAA) has been favored to reduce clinicians' cognitive workload during anesthesia maintenance. This systematic review, therefore, aimed to determine the comparative efficacy and safety of automated end-tidal controlled anesthesia (etCA) versus manually controlled anesthesia (MCA). METHODS: An electronic search was conducted across four databases: PubMed, Cochrane Central Register of Controlled Trials (CENTRAL), Web of Science, and Scopus. The retrieved references were then analyzed against the studies' screening and eligibility criteria before the full articles were included in the study. The Review Manager (RevMan 5.4.1) was then used to conduct the statistical analysis of the reported outcomes. RESULTS: The electronic search retrieved 965 articles, among which 10 were included in this review. The studies included a pooled sample size of 1269 who underwent surgical procedures under low-flow anesthesia. The analyzed evidence suggests with low certainty that the type of anesthesia control may not have any significant effect on both the anesthesia duration (SMD - 0.00 min; 95% CI [-0.34, 0.34] p = 0.99) and the procedural duration MCA (SMD 0.04 min; 95% CI; 95% CI [-0.26, 0.33] p = 0.80). Moreover, we found with low certainty in the evidence that the amount of sevoflurane consumed may not be significantly different between etCA and MCA (SMD - 0.32 mLs; 95% CI [-0.94, 0.29] p = 0.31). Our narrative synthesis indicated that etCA can potentially improve anesthesia delivery by reducing the required anesthetist interventions and ensuring stable and accurately administered anesthetics. CONCLUSION: This review found very low-quality evidence suggesting that the type of anesthesia control may not significantly affect anesthesia delivery parameters, such as the amount of anesthesia consumed and procedural and anesthesia duration. The analyses, however, had significant heterogeneity, making the estimates highly unreliable. On the other hand, some studies indicate potential advantages of using etCA, such as reducing the number of interventions required during anesthesia; however, the evidence remains limited, preventing a full exploration of these advantages.
15 May 2026
Read appraisal →Scientific reports
Deep learning-based automatic field of view planning for prostate MRI in oblique coronal and oblique axial planes
Accurate field-of-view (FoV) prescription in oblique coronal and axial planes is essential for high-quality prostate MRI but remains operator-dependent and variable. We developed and evaluated a ResNet-based deep learning framework for automated FoV planning. In this retrospective multicenter study, FoV prescriptions were annotated on PI-CAI dataset. Three readers assessed intra- and inter-rater variability to establish reference consistency. Three neural network variants were trained on 1,474 examinations from PI-CAI dataset (2012-2021), and the optimal model was selected by internal validation. Generalizability and clinical utility were tested on three external cohorts totaling 530 examinations (2021-2024) using a non-inferiority design. The selected model achieved non-inferior performance for slice positioning, with differences ranging from 0.16 ± 0.99 to 0.37 ± 0.48. Across sites, FoV overlaps ranged from 82.4 ± 4.1% to 88.7 ± 6.0%, and the angle differences between predicted and reference planes were 4.66 ± 4.89° (Site I), 3.46 ± 2.80° (Site II), and 2.99 ± 2.90° (Site III). Clinical utility was high at all sites, with acceptability rates of 97.9%, 97.7%,98.8%, 98.1% and 98.1% for Site I (Raters 1-5), 95.7%, 97.8%, 100%, 95.7% and 97.8% for Site II (Raters 1-5), and 100% for all raters at Site III. These findings demonstrate the feasibility of automated FoV positioning for prostate MRI and indicate excellent clinical utility.
14 May 2026
Read appraisal →Journal of medical Internet research
Barriers and Facilitators to Patient Acceptance of Artificial Intelligence in Health Care: Systematic Review
BACKGROUND: Artificial intelligence (AI) in the domain of health care is increasing in prominence. Acceptance is an indispensable prerequisite for the widespread implementation of AI. OBJECTIVE: This study aimed to explore barriers and facilitators influencing patients' acceptance of AI. METHODS: We conducted a systematic review following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Nine databases, including PubMed, Web of Science, and Embase, were comprehensively searched from inception to December 23, 2025. We included qualitative, quantitative, and mixed methods studies investigating adult patients' attitudes toward medical AI. Two researchers independently screened records, extracted data, and appraised methodological quality using the Mixed Methods Appraisal Tool. Following the Joanna Briggs Institute convergent integrated approach, data synthesis was guided by integrating the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) and the Theoretical Domains Framework (TDF). Factors were mapped to behavior change techniques (BCTs) and evaluated for practical feasibility using the Affordability, Practicability, Effectiveness and cost-effectiveness, Acceptability, Side-effects/safety, and Equity criteria. RESULTS: A total of 61 studies met the inclusion criteria out of 7452 search results. Study designs included qualitative (n=20), quantitative (n=35), and mixed methods (n=6). Performance and effort expectancies were the primary determinants of acceptance. Major barriers comprised perceived operational complexity, lack of algorithmic trust, reduced interpersonal interaction, privacy vulnerabilities, and high costs. Facilitators included transparent data governance, interpretability of AI decisions, improved clinician-patient communication, and human-centered design. Education level and disease severity emerged as key moderating variables. Through UTAUT2-TDF mapping, we identified 25 distinct BCTs (6 high, 14 medium, and 5 low feasibility) and formulated 40 actionable intervention strategies. CONCLUSIONS: This study innovatively integrates the UTAUT2 and TDF frameworks to evaluate patient acceptance of medical AI. Unlike existing reviews that predominantly evaluate isolated psychosocial factors or purely technical attributes, this transtheoretical approach differentiates itself by merging technology adoption mechanisms directly with behavioral drivers. Consequently, it contributes to the field by systematically identifying multilevel factors influencing acceptance, including performance expectancy, effort expectancy, and ethical security, and translating these into 40 actionable BCTs. In real-world clinical practice, these findings provide a feasible, prioritized blueprint for clinicians and administrators to design patient-centered interventions, enhancing the clinical integration and long-term effectiveness of medical AI.
12 May 2026
Read appraisal →PloS one
Gated subspace alignment with drift compensation for parameter-efficient Class-Incremental Learning
Class-Incremental Learning (CIL) aims to enable models to continuously learn new categories while preserving existing knowledge and avoiding catastrophic forgetting. Although parameter-expansion architectures can alleviate task interference to some extent, the representations of previously learned classes often drift or degrade as the feature subspaces continuously evolve and expand, resulting in decreased recognition performance for old classes. To address this issue, we propose an efficient CIL method-Dynamic Gated Adapter for Subspace Alignment (DGASA). Based on a frozen pre-trained backbone, DGASA introduces lightweight adapters with attention-based gating for each task to construct task-specific subspaces, while dynamically fusing cross-task information via attention mechanisms. In addition, DGASA learns a linear mapping between the old and new subspaces to achieve consistent alignment of old class prototypes in the current subspace without accessing past data. Extensive experiments demonstrate that DGASA significantly improves classification accuracy and resistance to forgetting on multiple benchmark datasets, offering strong generalization and computational efficiency.
12 May 2026
Read appraisal →Physics in medicine and biology
Multi-model study of fast VMAT segment dose calculation with deep learning.
Objective.Deep learning (DL) methods enable photon dose calculation under two main coordinate representations: Beam's Eye View (BEV) and patient coordinates. We evaluate dose calculation accuracy and speed under these coordinate paradigms and with representative DL models within a unified dataset and pipeline, and introduce two lightweight models for fast photon dose calculation.Approach.Planning computed tomography (CT) scans and volumetric modulated arc therapy plans from 24 prostate cancer patients were used. Monte Carlo simulation generated 5940, 540, and 3053 segment doses for training (11 patients), validation (3), and testing (10), respectively. For BEV, we used a combination of convolutional neural network (CNN) and convolutional long short-term memory network (ConvLSTM) called CNN-ConvLSTM, a CNN-Mamba combination (CNN-Mamba), a transformer-based architecture (DoTA), and a cascaded 3D UNet (C3D). These were trained on CT and segment-projection BEV cuboids. For patient coordinates, the DeepDose individual segment dose prediction framework implemented with C3D (DeepDose-C3D) was trained on cropped CT volumes with four physical inputs. Segment and plan dose accuracy were assessed using local gamma passing ratesγPR(2%/3 mm and 1%/3 mm) and dose-volume histogram metrics. Dose calculation times (inference plus pre/post-processing) were measured on three different graphics processing unit (GPUs).Results.All five models achieved mean localγPRvalues⩾91.0% (2%/3 mm) for segment doses and⩾99.0% (1%/3 mm) for plan doses. Mean per-segment dose calculation times were 79, 67, 298, 490, 356 ms for CNN-ConvLSTM, CNN-Mamba, DoTA, C3D, and DeepDose-C3D, respectively. On the latest-generation GPU available, the corresponding per-plan (average 305 segments) dose calculation times were 5.5, 6.2, 33.6, 38.7, 35.4 s.Significance.Both BEV- and patient-coordinate DL methods achieved accurate photon plan dose calculation, with BEV-based approaches showing more robust segment performance. CNN-ConvLSTM and CNN-Mamba retain comparable accuracy at lower computational cost, enabling fast photon dose calculation.
12 May 2026
Read appraisal →Analytical chemistry
Quantitative Analysis of Multiple Serum Tumor Biomarkers by an Interpretable Stacked Ensemble Model
Surface-enhanced Raman spectroscopy (SERS) offers exceptional sensitivity and specificity for biomolecular detection, particularly in analyzing serum tumor biomarkers. However, its application is hindered by spectral complexity and matrix interference. Although machine learning (ML) has shown promise in SERS data analysis, existing quantitative models for biomarker detection lack generalizability and interpretability. To address these challenges, we developed an interpretable stacked ensemble model (ISEM) that integrates feature selection, ensemble learning, and explainable artificial intelligence (XAI) for quantifying 12 serum tumor biomarkers. To ensure reliable quantitative analysis, we validated the acquired SERS data using uniform and reproducible Au nanoparticle substrates. Preprocessing, least absolute shrinkage, and selection operator (LASSO) feature selection were employed to establish a foundation for accurate quantification. Subsequently, 12 serum tumor biomarkers were quantified, demonstrating the superior performance of ISEM, which achieved a high predictive accuracy, with R2 values exceeding 0.9 for all biomarkers. Crucially, based on the quantitative results, we provided molecular-level interpretability for structure-spectrum correlations using Shapley additive explanations (SHAP), revealing how glycosylation reactions, matrix interference, and spectral overlap influence prediction accuracy. Furthermore, the capability of ISEM to quantify multiple biomarkers in unseen samples was confirmed through validation of its generalizability. Our study establishes an ensemble-driven, interpretable framework for quantitative biomarker analysis in a complex biological matrix, demonstrating significant potential for early cancer diagnosis and screening.
11 May 2026
Read appraisal →Journal of bronchology & interventional pulmonology
AIBAN-AI Bronchial Detection and Airway Navigation System
BACKGROUND: Accurate navigation within the lungs is critical for bronchoscopy procedures but remains challenging due to the complex airway anatomy. Artificial intelligence (AI) integrating real-time landmark detection and navigation guidance may enhance precision and safety. METHODS: We developed an AI Bronchial detection and Airway Navigation system (AIBAN) for bronchoscopy, combining airway lumen detection, anatomic localization, and navigation guidance. A YOLOv8-based detection module was used to identify 34 airway landmarks in real time. Next, an airway-level localization module estimated the bronchoscope's position using a hierarchical anatomic graph, identifying the deepest branch compatible with all coherent detections in each frame. A navigation module then recommended the next airway to follow along predefined anatomic paths, providing directional cues to perform a complete and systematic bronchoscopy. RESULTS: AIBAN was developed on a data set of 18 bronchoscopy videos, using 15 for training and 3 for validation. In validation, the framework correctly identified the bronchoscope's location in 91.1% of the frames. In addition, 92.6% of predictions were within one airway branch of the true location, and 94.5% were within 2 branches. The navigation guidance performance was also promising, with the system following the correct anatomic pathway in 96.2% of test videos, demonstrating reliable guidance through the lungs. CONCLUSION: AIBAN enables accurate airway detection, localization, and navigation guidance in a simulation-based setting. It can provide reliable guidance along predefined paths and precise spatial positioning and is a promising tool for medical training and has potential for future clinical implementation.
11 May 2026
Read appraisal →Journal of diabetes science and technology
Diabetes-Technology and Waste: The Future is Now.
The downside of the success story of diabetes technology is the amount of waste generated by manufacturing and using such medical products. Quantitative surveys performed in the United States and Germany document how many pounds of plastic waste, batteries, and electronic parts are piling up per patient with diabetes each year; however, the actual environmental burden of diabetes care has not been assessed yet. Given the highly probable further increase in usage of diabetes technology devices, what are the options to change the situation? Ideally, one would avoid generating waste by optimizing the design of the products (Eco-Design); however, this approach faces a number of complex regulatory and economic hurdles. The issue of waste management is becoming increasingly important as the use of wearable devices increases. Stewardship of the environment will require all stakeholders to address waste management.
4 May 2026
Read appraisal →ACS sensors
Multifunctional Wearable OLED-OPD System with Wavelength-Tunable Emission for Phototheranostics
The growing demand for noninvasive wearable healthcare technologies calls for compact platforms capable of integrating diagnostic sensing and therapeutic light delivery within a single device. Conventional systems typically rely on spatially separated light sources or rigid inorganic LEDs, leading to inefficient area usage and limited mechanical compatibility with soft biological tissues. Here, we present a multifunctional wearable optoelectronic platform based on wavelength-tunable organic light-emitting diodes (WTOLEDs) integrated with organic photodiodes (OPDs). The vertically stacked red and blue OLED structure enables polarity-dependent wavelength switching under AC driving, generating alternating emissions at 624 nm and 464 nm from a single compact device without lateral source separation. The surrounding geometry, in which the light-emitting region encircles the light-receiving element, provides an area-efficient optical interface that enhances optical coupling and signal sensitivity within a flexible form factor. Using this platform, we demonstrate dual-wavelength physiological monitoring via photoplethysmography (PPG) and therapeutic applications, including red-light photobiomodulation and blue-light-mediated bilirubin photodegradation. The device further exhibited stable thermal and mechanical performance under wearable operating conditions. These results establish a compact phototheranostic architecture enabling wavelength-selective sensing and therapy within a single wearable platform.
26 Apr 2026
Read appraisal →ACS sensors
Laser-Engineered MXene Heterostructure for Wearable Ammonia Sensors
Exhaled ammonia is a vital biomarker for clinical diagnosis of hepatorenal dysfunction and metabolic disorders. However, conventional sensors are often limited by complex fabrication processes, high operating temperatures, and particularly, compromised selectivity under high-humidity conditions. To overcome these challenges, we developed a laser-assisted heterostructure engineering strategy that enables in situ construction of Schottky barrier in MXene/polyacrylonitrile (PANF) nanofiber membranes, termed as a laser-engineered MXene (LEM) sensor. The laser micropatterning technique provides three key innovations: (1) kinetically controlled deposition of MXene (Ti3C2Tx) with minimal oxidation, (2) self-aligned formation of Schottky barriers through interfacial TiO2/MXene heterojunctions, and (3) creation of hierarchical gas transport channels within the 3D nanofibrous matrix. The optimized LEM sensor exhibits outstanding sensitivity (2.5% ppm-1), a low detection limit (0.2 ppm) at room temperature (25 °C), and humidity operation stability (> 10% response at 90% relative humidity). This performance surpasses that of conventional MXene-based sensors by fivefold in sensitivity without the need for thermal activation. Mechanism studies reveal that the laser-induced heterointerface facilitates charge transfer-dominated ammonia adsorption and desorption kinetics, while the nanofibrous architecture ensures molecular accessibility to active sites. This ambient-processable and scalable fabrication method paves the way for advanced wearable breath analyzers by combining manufacturing versatility, humidity-resistant excellent selectivity, and energy-efficient operation.
26 Apr 2026
Read appraisal →Biomedical physics & engineering express
Evaluating the impact of reconstruction algorithms on digital breast tomosynthesis system optimization for microcalcification detection
Early detection of microcalcifications in digital breast tomosynthesis (DBT) depends on optimizing both image reconstruction algorithms and system design parameters. Thisin-silicostudy evaluates the combined effects of x-ray tube motion, angular dose distribution, and detector characteristics on reconstruction algorithms for microcalcification detectability. A synthetic breast phantom containing 127 microcalcification clusters was generated and imaged under four acquisition conditions varying in tube motion (continuous vs. step-and-shot), angular dose distribution (uniform vs. non-uniform), and detector performance (noise level and pixel size). Projection data were reconstructed using filtered back-projection (FBP) and the simultaneous algebraic reconstruction technique (SART). Detection performance was assessed using a 2D Filtered Channel Observer in a multi-reader, multi-case study. Evaluation metrics included signal-to-noise ratio and area under the receiver operating characteristic (ROC) curve. Results show that SART performs best under step-and-shot acquisition by minimizing motion blur and enhancing fine detail, while FBP benefits more from non-uniform angular dose distributions. Detector improvements-lower noise and finer resolution-enhance detectability across all configurations, with FBP showing the largest gains. These findings highlight the interdependence of reconstruction algorithm, tube motion, dose strategy, and detector design in DBT system optimization. This study provides a detailed investigation into classical reconstruction strategies and establishes a methodological baseline for future DBT optimization research, offering actionable guidance for acquisition protocol design and hardware development.
24 Apr 2026
Read appraisal →PloS one
A deep reinforcement based echo state network for network intrusion classification
Network intrusion classification referred to the process of monitoring and analyzing network traffic to identify suspicious activities or attacks. In this work, author proposed a novel approach to classify network intrusion by utilizing deep reinforcement learning (DRL), integrating a reservoir computing approach Echo State Network (ESN). A DRL-based approach improved upon traditional deep learning by adapting dynamically to novel/unknown and evolving attack patterns. Unlike static models, DRL continuously learned optimal strategies through interaction with the environment, allowing for better detection of previously unseen threats in real-time. To address the class imbalance often encountered in network intrusion datasets, we evaluated the performance of several advanced data balancing techniques, including Borderline-SMOTE, SMOTE-ENN, ADYSN, and K-means SMOTE. The findings demonstrated that the K-means-based data balancing method outperformed other techniques, resulting in the most robust performance across various metrics. Author conducted multi-dataset validation on benchmark datasets like NF-BoT-IoT, NF-UNSW-NB15, NF-ToN-IoT, NF-ToN-IoT-v2, NF-CSE-CIC-IDS2018 and NF-UNSW-NB15-v3 to ensure robustness across different network flow data. For adaptive modeling testing, author excluded some attack types from training data and included them in testing data (e.g., DoS, Backdoor attacks were excluded from the training data but included in the testing data (see Table 3)). The proposed approach enhanced the accuracy and reliability of intrusion detection, making it a viable solution for securing modern network infrastructures. The source code of this work is available in this Github repository (https://github.com/codewithkhurshed/DRLZDNIDS).
18 Apr 2026
Read appraisal →Sleep
Mamba-based deep learning approach for sleep staging on a wireless multimodal wearable system without electroencephalography
STUDY OBJECTIVES: We investigate a Mamba-based deep learning approach for sleep staging on signals from ANNE One (Sibel Health, Chicago, IL), a non-intrusive dual-module wireless wearable system measuring chest electrocardiography, triaxial accelerometry, chest temperature, and finger photoplethysmography and finger temperature. METHODS: We obtained wearable sensor recordings from 357 adults undergoing concurrent polysomnography at a tertiary care sleep lab. Each polysomnography recording was manually scored, and these annotations served as ground truth labels for training and evaluation of our models. Polysomnography and wearable sensor data were automatically aligned using their electrocardiography channels with manual confirmation by visual inspection. We trained a Mamba-based recurrent neural network architecture on these recordings. Ensembling of model variants with similar architectures was performed. RESULTS: After ensembling, the model attains a 3-class (wake, non-rapid eye movement sleep, rapid eye movement sleep) balanced accuracy of 84.02 per cent, F1 score of 84.23 per cent, Cohen's κ of 72.89 per cent, and a Matthews correlation coefficient (MCC) score of 73.00 per cent; a 4-class (wake, light NREM [N1/N2], deep NREM [N3], REM) balanced accuracy of 75.30 per cent, F1 score of 74.10 per cent, Cohen's κ of 61.51 per cent, and MCC score of 61.95 per cent; a 5-class (wake, N1, N2, N3, REM) balanced accuracy of 65.11 per cent, F1 score of 66.15 per cent, Cohen's κ of 53.23 per cent, MCC score of 54.38 per cent. CONCLUSIONS: Our Mamba-based deep learning model can successfully infer major sleep stages from the ANNE One, a wearable system without electroencephalography, and can be applied to data from adults attending a tertiary care sleep clinic.
17 Apr 2026
Read appraisal →Biomedical physics & engineering express
Bluetooth low energy thermohygrometric sensor for continuous respiratory rate monitoring: 48 h breathing simulator and human proof-of-concept
Continuous respiratory rate monitoring is essential for early detection of clinical deterioration, but conventional methods like capnography are often impractical outside high-acuity settings. We developed a low-cost, Bluetooth Low Energy (BLE) respiratory rate monitor incorporating a MEMS temperature-humidity sensor (AHT21) and Nordic nRF52840 microcontroller, designed to attach externally to a standard oxygen mask. The device detects breaths using rapid, cyclic changes in exhaled temperature and humidity via a slope-based time-domain algorithm. Bench validation over 48 h using a breathing simulator (5-60 breaths min-1, tidal volume 300 ml, 34 °C, 95%-100% RH) demonstrated 96.4% overall breath detection accuracy and strong correlation with programmed rates (r = 0.996, P < 0.0001). Sensor saturation occurred at high respiratory rates (60 breaths min-1), and high ambient humidity levels (90% RH) both of which reduced measurement accuracy. Human testing in three healthy adults showed strong agreement with capnography (mean bias 0.44 breaths min-1; 95% limits of agreement -1.86 to 2.75), with minor under-detection at the highest rate (50 breaths min-1). The device operated reliably across oxygen flow rates of 4-15 l min-1, with reduced performance at 0-2 l min-1due to saturation of the sensor. These results demonstrate that BLE-enabled MEMS thermohygrometric sensors can accurately monitor respiratory rate under controlled conditions while highlighting operational limits due to sensor saturation. This study extends prior work on humidity-based respiratory monitoring and illustrates the potential for low-cost, portable respiratory rate devices suitable for low-acuity or resource-limited clinical settings.
9 Apr 2026
Read appraisal →Physics in medicine and biology
Fraction-variant VMAT planning for patients with complex gynecological and head-and-neck cancer.
Objective. Increasing the number of arcs in volumetric modulated arc therapy (VMAT) allows for better intensity modulation and may improve plan quality. However, this leads to longer delivery times, which may cause patient discomfort and increase intra-fractional motion. In this study, it was investigated whether the delivery of different VMAT plans in different fractions may improve the dosimetric quality and delivery efficiency for the treatment of patients with complex tumor geometries.Approach. A direct aperture optimization algorithm was developed which allows for the simultaneous optimization of different VMAT plans to be delivered in different fractions, based on their cumulative physical dose. Each VMAT plan is constrained to deliver a uniform dose within the target volume, such that the entire treatment does not alter the fractionation scheme and is robust against inter-fractional setup errors. This approach was evaluated in-silico for five patients with gynecological and five patients with head-and-neck cancer.Main results. For all patients, fraction-variant treatments achieved significantly better target coverage and reduced the dose to critical organs-at-risk compared to fraction-invariant treatments that deliver the same plan in every fraction, where the dosimetric benefit was shown to increase with the number of different plans to be delivered. In addition, 1-arc and 2-arc fraction-variant treatments could approximate the dosimetric quality of 3-arc fraction-invariant treatments, while reducing the delivery time.Significance.Fraction-variant VMAT treatments may achieve excellent dosimetric quality for patients with complex tumor geometries, while keeping the delivery time per fraction viable.
9 Apr 2026
Read appraisal →Progress in biomedical engineering (Bristol, England)
Trustworthy AI in digital health: a comprehensive review of robustness and explainability
Ensuring trust in artificial intelligence (AI) systems is essential for the safe and ethical integration of machine learning systems into high-stakes domains such as digital health. Key dimensions, including robustness, explainability, fairness, accountability, and privacy, need to be addressed throughout the AI lifecycle, from problem formulation and data collection to model deployment and human interaction. While various contributions address different aspects of trustworthy AI, a focused synthesis on robustness and explainability, especially tailored to the healthcare context, remains limited. This review addresses that need by organizing recent advancements into an accessible framework, highlighting both technical and practical considerations. We present a structured overview of methods, challenges, and solutions, aiming to support researchers and practitioners in developing reliable and explainable AI (XAI) solutions for digital health. This review article is organized into three main parts. First, we introduce core pillars of trustworthy AI and discuss the technical and ethical challenges they pose, particularly in the context of digital health. Second, we explore application-specific trust considerations across domains such as intensive care, mental health, metabolic disease, and public health surveillance, highlighting how explainability, clinical validation, and human oversight support trust. Lastly, we present recent advancements in techniques aimed at improving robustness under data scarcity and distributional shifts, as well as XAI methods ranging from feature attribution to gradient-based interpretations and counterfactual explanations. This paper is further enriched with detailed discussions of the contributions toward robustness and explainability in digital health, the development of trustworthy AI systems in the era of large language models, and various evaluation metrics for measuring trust and related parameters such as validity, fidelity, and diversity, offering a roadmap for building safer and more reliable AI systems.
8 Apr 2026
Read appraisal →Ultrasound in medicine & biology
From Recognition to Action: Integrating Deep Learning and Robotic Control in Transthoracic Echocardiography
Population aging has driven a rise in heart failure cases, increasing the clinical burden on cardiac diagnostics. As a first-line imaging method, transthoracic echocardiography (TTE) faces limitations due to operator dependence, patient variability, and workflow inefficiencies. Meanwhile, advances in artificial intelligence (AI) and robotic ultrasound systems offer new potential pathways toward automated diagnosis. This review examines the current landscape of AI-based image analysis and robotic-assisted echocardiography. It presents a detailed analysis of advancements in artificial intelligence (AI) applied to echocardiography and the evolution of robotic ultrasound systems, aiming to introduce a discussion on semantic-to-motion mapping. By synthesizing recent progress and outlining future directions, we can correctly recognize the current maturity level of artificial intelligence development in the field of ultrasound examination and prepare well for the subsequent work.
4 Apr 2026
Read appraisal →Eur Heart J
Coronary revascularization: a long-term perspective.
This paper will forecast advancements in coronary revascularization by 2040, drawing on historical trends and recent breakthroughs. Having forecasted in 2000 the impact of innovations like drug-eluting stents, coronary computed tomography angiography, and bioresorbable scaffolds, the authors examine the evolving landscape of coronary artery disease treatment emphasizing artificial intelligence and omics sciences. Imagenomics-the integration of imaging and omics-will be a transformative tool with artificial intelligence enabling more personalized decisions between pharmacological and mechanical revascularization. The shift towards minimally invasive, image-guided procedures is discussed along with the potential obsolescence of current antiplatelet and anticoagulant therapies with novel biomimetic peptides like CD31-covalently bound to metallic or polymeric devices. Advances in photon-counting computed tomography and Fibre Optic Real Shape promise high-resolution imaging with lower radiation, enhancing procedural safety and diagnostic accuracy. It is anticipated that robotics and 3D holograms in percutaneous and surgical revascularization will improve precision and the resulting outcomes. Additionally, the need for mechanical revascularization for younger patients may decline due to effective plaque regression therapies and novel potent anti-atherogenic biologics. However, the aging population will drive demand for mechanical interventions, with advancements in atherectomy and lithotripsy techniques improving outcomes in complex, calcified lesions. This comprehensive analysis outlines a future where myocardial revascularization becomes increasingly personalized, with technology-driven interventions redefining cardiovascular medicine.
1 Feb 2026
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