Machine learning and its current and future applications in the management of vitreoretinal disorders

Abstract

ABSTRACT Introduction In recent decades, there have been significant advances in the field of Artificial intelligence (AI), retinal imaging, and therapeutics. The specialty of retina generates huge datasets, which are ideally suited to create robust AI models for early detection, diagnosis, classification, and treatment of retinal diseases. Areas Covered Basic aspects of AI algorithms, machine learning models, application of AI in diabetic retinopathy (DR), retinopathy of prematurity (ROP), retinal vascular occlusion (RVO) and age-related macular degeneration (AMD) have been described, highlighting findings from important studies. Literature Review and Methodology Comprehensive search of indexed medical literature on Medline/PubMed and Google Scholar databases. The search terms included artificial intelligence, deep learning, machine learning in DR, AMD, ROP, retinal vascular disease, and RVO. The manuscripts published in English literature in the last two decades were selected for this review. Expert Opinion Several AI algorithms have been developed which are accurate and efficacious in screening, detecting, diagnosing, and aiding in managing patients with various retinal diseases. Proper external validation using large datasets and establishing their accuracy is central to increasing the confidence and acceptance of these algorithms. The application of AI to screening models can be a boon in many environments, but particularly resource-depleted settings.