AI Frameworks for the Early Detection and Management of Diabetic Retinopathy: A Real-World Application

Diabetic retinopathy (DR) is a condition that progressively affects the microvasculature, commonly seen in individuals with diabetes, which is a leading cause of visual impairment across the globe. Approximately one-third of people with diabetes will eventually develop DR, and it remains a public health issue because the retinal damage is irreversible if the disease continues to go undiagnosed and untreated. Although various screening methods exist, including fundus photography and optical coherence tomography (OCT), they are often limited as a result of availability and expertise required for diagnosis and management. Recent advancements in artificial intelligence (AI) are transforming DR screening and diagnosis. AI has demonstrated the potential to automate retinal image evaluation with accuracies that, in some studies, approach or exceed existing standards of care; however, reported performance varies across algorithms, datasets, and clinical settings, highlighting the need for cautious interpretation and further validation in diverse populations. Machine learning (ML) & deep learning (DL) models, particularly convolutional neural networks (CNNs), have been found to have excellent performance in identifying DR-related abnormalities, including mild, moderate, and severe DR, and diabetic macular edema, at sensitivities and specificities exceeding traditional screening. AI tools under various names, such as IDx-DR, EyeArt, and Google's AI algorithm, are quick and price-effective tests using an AI solution for early diagnosis of DR in either developed or developing economies. In addition, AI can also support predictive analytics, risk stratification, and treatment decision-making, so that ophthalmologists can provide patients with better management options. Importantly, AI also comes with its own challenges, such as data harmonization and regulatory approval for deploying AI in clinical care. This review will focus on the existing literature on the applications of AI in diagnosing and management of DR and will outline the importance of this innovation.