Age-related macular degeneration is a major retinal condition in which timely diagnosis and continuous monitoring are essential for preserving vision. Artificial intelligence is increasingly being investigated for the analysis of retinal images and identification of structural changes associated with different stages of AMD. AI-based approaches may support earlier recognition and more consistent assessment of disease characteristics.
This session will explore the application of machine learning and deep learning to fundus photography, optical coherence tomography, OCT angiography, and multimodal retinal imaging. Discussions will address automated detection of drusen, retinal fluid, pigmentary changes, geographic atrophy, and other imaging biomarkers associated with AMD progression.
The session will also examine the potential of AI for predicting disease progression and treatment response while addressing challenges related to clinical validation, longitudinal monitoring, data diversity, and algorithm reliability. Experts will discuss how intelligent technologies can complement clinical assessment and contribute to personalized approaches to AMD management.