Diabetic Retinopathy Detection: AI Models and Approaches.

Abstract

Background: Diabetic retinopathy (DR), a major cause of vision loss worldwide, results from chronic diabetes damage to retinal blood vessels. Vision loss can be prevented if DR is detected early, but traditional retinal screening by eye care takes time and expertise. Recent advances in AI technology, including classical machine learning and deep learning, can be more accurate in DR detection. This article provides a comprehensive review of current AI models and approaches of DR screening.

Methods: We searched PubMed, Web of Science, Scopus, ScienceDirect, and EBSCOhost using the keywords: diabetes, retinopathy, screening, and early detection. The search was limited to English language and studies published between 2020 and 2025.

Results: The findings suggest that AI models have become crucial for early DR diagnosis. While traditional machine learning previously lacked effectiveness, deep learning has now significantly improved diagnostic performance. The models, such as the URNet system, the vision transformer (ViT) model, the ResNet-50 and EfficientNetB0 models, the DenseNet model, and the ResNet-18 model, have achieved high-performance metrics using publicly available datasets. DR screening devices, like ADX-DR, have shown commendable performance. The EyeArt modality demonstrated exceptional sensitivity across diverse populations, detecting around 98.5% of vision-threatening DR, while Google AI matched specialist performance in specificity and surpassed it in sensitivity.

Conclusion: AI methods using deep learning frameworks such as CNNs have attained expert-level accuracy in DR classification, in addition to real-world validation. Semiautonomous systems like the IDx-DR and EyeArt have robust clinical performance and scalability, especially in countries with few ophthalmologists. Although research has been mainly conducted in Asia, there is a lack of research from Africa and low-income countries. Future techniques, including ensemble models and federated learning, will enhance accuracy and reliability further, aiding early diagnosis and prevention of vision loss globally.