Impact of Mydriasis on Image Gradability and Automated Diabetic Retinopathy Screening with a Handheld Camera in Real-World Settings

PurposeDiabetic retinopathy (DR) screening in low- and middle-income countries (LMICs) faces challenges due to limited access to specialized care. Portable retinal cameras provide a practical alternative, but image quality, influenced by mydriasis, affects artificial intelligence (AI) model performance. This study examines the role of mydriasis in improving image quality and AI-based DR detection in resource-limited settings. MethodsWe compared the proportion of gradable images between mydriatic and non-mydriatic groups and used logistic regression to identify factors influencing image gradability, including age, gender, race, diabetes duration, and systemic hypertension. A ResNet-200d algorithm was trained on the mBRSET dataset and validated on mydriatic and non-mydriatic images. Performance metrics, such as accuracy, F1 score, and AUC, were evaluated. ResultsThe mydriatic group had a higher proportion of gradable images (82.1% vs. 55.6%, P< 0.001). Factors such as systemic hypertension, older age, male gender, and longer diabetes duration were associated with lower image gradability in non-mydriatic images. Mydriatic images achieved better AI performance, with accuracy (82.91% vs. 79.23%), F1 score (0.83 vs. 0.79), and AUC (0.94 vs. 0.93). Among gradable images, the performance difference was not statistically significant. ConclusionMydriasis improves image gradability and enhances AI model performance in DR screening. However, optimizing AI for non-mydriatic imaging is critical for LMICs where mydriatic agents may be unavailable. Refining AI models for consistent performance across imaging conditions is essential to support the broader implementation of AI-driven DR screening in resource-constrained settings.