Glaucoma management depends on accurate assessment of structural and functional changes within the eye, often requiring interpretation of multiple clinical measurements over time. Artificial intelligence is being investigated as a tool for analyzing optic nerve images, retinal nerve fiber layer measurements, visual fields, and other ophthalmic parameters. These technologies may support earlier identification and improved monitoring of glaucoma.
This session will explore AI-based approaches for glaucoma detection, risk assessment, progression analysis, and personalized management. Scientific discussions will consider the use of machine learning for optic-disc evaluation, retinal nerve fiber layer analysis, visual-field interpretation, and integration of multimodal clinical information.
Attention will also be given to the challenges of applying AI to a complex and heterogeneous disease such as glaucoma. Experts will discuss longitudinal datasets, model interpretability, clinical validation, population diversity, and integration into existing clinical workflows, while examining how AI can complement ophthalmologists in delivering more informed and personalized glaucoma care.