Artificial Intelligence for Diabetic Retinopathy Screening: Performance of Deep Learning Models

Abstract Background Diabetic Retinopathy (DR) remains a leading cause of preventable blindness, with rising prevalence particularly in low-resource settings. Traditional DR screening relies on specialist ophthalmologic assessment, posing barriers to early detection. Recent advances in deep learning have enabled the development of automated tools that may enhance the scalability and accessibility of DR screening. Objectives To compare the performance of two convolutional neural network (CNN) architectures-EfficientNet-B5 and ResNet-50-for automated DR detection using publicly available retinal fundus image datasets. Methods We utilized a transfer learning approach on two large datasets: EyePACS 2015 (n = 35,126) for training/validation and APTOS 2019 (n = 3,662) for external testing. Images were preprocessed with resizing, cropping, and contrast enhancement. The EfficientNet-B5 and ResNet-50 models were independently trained and evaluated using key metrics: accuracy, sensitivity, specificity, area under the ROC curve (AUC), and Quadratic Weighted Kappa (QWK), with classification across DR severity levels. Results EfficientNet-B5 achieved a QWK of 92%, sensitivity of 98%, specificity of 93%, and AUC of 96% on the test dataset. ResNet-50 demonstrated similarly high performance with an accuracy of 96%, QWK of 86%, sensitivity of 94%, specificity of 97%, and AUC of 96%. Both models showed robust generalization across severity grades, indicating strong discriminative capabilities. Conclusions The results highlighted that our models are viable architectures for AI-driven DR screening, with EfficientNet-B5 showing slightly higher agreement scores and ResNet-50 offering stronger specificity. These models hold promise for deployment in DR screening programs, especially in resource-limited settings where ophthalmologic expertise is scarce. Future work should focus on clinical validation and integration into public health systems to improve early DR detection and reduce vision-related morbidity. Key messages • Integrating artificial intelligence (AI) into diabetic retinopathy screening can enhance early detection and support equitable access to eye care, particularly in resource-limited settings. • AI-based screening tools offer a scalable and effective approach to strengthen public health strategies aimed at preventing vision loss from diabetic complications.