Ophthalmic Imaging AI: Computer Vision for Retinal Disease Prediction

Artificial intelligence is rapidly expanding the diagnostic value of ophthalmic imaging, transforming retinal photographs and optical coherence tomography (OCT) scans into rich sources of predictive clinical insight. As deep learning and computer vision methods mature, retinal imaging is increasingly being used not only to document ocular abnormalities, but also to identify subtle biomarkers associated with progressive diseases such as glaucoma and other vision-threatening conditions. This evolution positions ophthalmology as one of the most promising areas for practical AI-assisted screening and longitudinal monitoring. 

This session will provide attendees with a focused look at how fundus photography and OCT are being used to build and validate predictive AI models for ophthalmic disease detection. Participants will gain an understanding of the imaging features, annotation requirements, and machine learning workflows that support deep learning-based retinal analysis, as well as how these models are being trained to improve diagnostic sensitivity, risk stratification, and clinical decision support. Presenters will also examine current advances in automated glaucoma prediction and related ophthalmic applications where AI is demonstrating measurable clinical potential. 

Equally important, the session will address the barriers that continue to limit real-world adoption of ophthalmic AI. Attendees will explore practical challenges surrounding generalizability across populations and imaging devices, model interpretability, workflow integration, and clinician trust. By the end of the session, participants will better understand both the technical promise and the implementation realities of deploying AI-powered retinal imaging tools in modern ophthalmic care environments. 

Objectives

• Describe how deep learning and computer vision techniques are applied to fundus photography and OCT for ophthalmic disease prediction  
• Identify key technical and clinical barriers affecting the development, validation, and deployment of retinal AI models  
• Explain the role of AI-driven retinal analysis in supporting earlier detection, risk stratification, and ophthalmic decision support  
• Differentiate considerations for integrating ophthalmic AI tools into routine clinical imaging workflows