Retinal imaging has become one of the most important areas for the application of artificial intelligence in ophthalmology. Advanced imaging modalities generate detailed information about retinal structures and pathological changes, providing valuable datasets for computational analysis. AI-based systems are increasingly being investigated for automated detection, classification, segmentation, and monitoring of retinal diseases.
This session will examine the role of artificial intelligence in analyzing fundus photographs, optical coherence tomography, OCT angiography, and other retinal imaging modalities. Discussions will cover emerging approaches for diabetic retinopathy, age-related macular degeneration, retinal vascular disorders, macular diseases, and other conditions affecting retinal health.
The session will further explore the clinical translation of AI-powered retinal imaging technologies, including screening applications, longitudinal disease monitoring, image-quality assessment, and clinical decision support. Experts will consider the importance of diverse datasets, rigorous validation, clinical integration, and collaboration between ophthalmologists, imaging specialists, and technology researchers.