Measuring and reducing surgical staff stress in a realistic operating room setting using EDA monitoring and smart hearing protection

BackgroundStress is a critical factor in the operating room (OR) and affects both the performance and well-being of surgical staff. Measuring and mitigating this stress

Gradient-specified optimization based on muscle surface mesh and moment arm as an effect-oriented approach of automated musculotendon path modeling

There is more to musculotendon path modeling than aligning a cable to reflect the geometric features of a muscle-tendon unit. From the perspective of simulation

Adaptation to free-living drives loss of beneficial endosymbiosis through metabolic trade-offs

Symbioses are widespread (1) and underpin the function of diverse ecosystems (2-6), but their evolutionary stability is challenging to explain (7,8). Fitness trade-offs between con-trasting

Hippocampal representations differentiate reactive and anticipatory responses during foraging under threat

Adaptive behavior under threat requires balancing reward pursuit against the risk of harm. During approach-avoidance conflict, animals often pause at decision points, but whether these

Frontal Brain Injury Reduces Sensitivity to Reward-Predictive Cues and Remodels the Nucleus Accumbens

Traumatic brain injuries (TBIs) are more than mere lesions and generate a persistent secondary pathology. This, combined with functional reorganization of circuits post-injury, may explain

Imbalance-Aware Optimal Transport Learning for Cost-Effective Diabetic Retinopathy Screening

April 18, 2026

Abstract Background Diabetic Retinopathy (DR) is one of the leading cause of vision loss and blindness. AI models have been instrumental in providing an alternative solution to real-life medical treatment which are costly and sometimes not readily available in developing and underdeveloped nations. However, most of the existing AI models are developed with high-quality clinical images that makes it difficult to use such models in low-resource settings. For this reason, this research focus on bridging this gap by developing a low-resource, mobile-friendly, and deployable deep learning (DL) model for the detection of DR using an imbalance-aware optimal transport (OT) learning approach. Methods We trained our proposed framework with both high-quality hospital- grade images and low-resource smartphone-acquired images, and evaluated with the original test set from the smartphone domain. We also curated three levels of smart- phone image-degradation quality and reported results from multiple experiments with bootstrapping. All model evaluations were assessed using the AUC, Sensitivity, and Specificity. Our results were compared with empirical risk minimization (ERM), Prototype OT, and Sinkhorn OT methods. Results We used four strong backbone architectures in the assessment. With our framework, Mobilevit-s achieved the best performance: an AUC of 87%, sensitivity of 89%, and specificity of 95%. Meanwhile, the statistical significance performance test (95% CI) shows that the AUC results are in the range of approximately 84% to 89%. For sensitivity, the range is 81% to 96%, and for specificity, 93% to 96%. This result indicated a performance increase of more than 3-5% compared to baseline methods. Conclusion Our framework shows promising results for low-resource DR screening, which has a potential to benefit less-advantaged groups and developing nations. Keywords Diabetic retinopathy, cost-effective AI, optimal transport, smartphone screening, deep learning.

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