Digital fundus imaging provides a valuable source of visual information for screening, diagnosis, and monitoring of numerous ophthalmic conditions. Advances in computer vision have enabled sophisticated analysis of fundus photographs, allowing algorithms to identify patterns and structural abnormalities that may assist ophthalmic evaluation.
This session will explore computer-vision techniques for automated analysis of retinal photographs, including image classification, segmentation, lesion detection, and feature extraction. Applications across diabetic retinopathy, glaucoma, AMD, retinal vascular diseases, and other ocular disorders will be examined from both research and clinical perspectives.
Discussions will also address image standardization, image-quality assessment, algorithm performance, dataset diversity, and integration with clinical workflows. The session will highlight the potential of computer vision to support scalable ophthalmic screening while maintaining appropriate specialist oversight and clinical validation.