Harnessing Machine Learning for Early Detection and Prediction of Ocular Diseases: Current Trends and Future Directions

Ocular diseases pose significant challenges to global health, emphasizing the critical need for early detection and prediction to mitigate their impact. This review explores the application of machine learning (ML) and data-driven approaches in predicting ocular diseases. Data sources encompassing imaging, genetic, and clinical data are scrutinized, alongside challenges in acquisition, integration, and preprocessing, with ethical considerations paramount. Applications focus on glaucoma, age-related macular degeneration (AMD), and diabetic retinopathy, elucidating prediction models tailored to each disease’s unique characteristics and incorporating genetic and environmental factors. Evaluation metrics such as sensitivity, specificity, and ROC curves are reviewed, emphasizing the importance of rigorous validation techniques like cross-validation. Current challenges in ocular disease prediction models are outlined, alongside future opportunities such as integrating multimodal data and enabling real-time monitoring. Regulatory and ethical considerations underscore the need for responsible data handling.