Optical coherence tomography has become an essential imaging modality in contemporary ophthalmology, providing detailed cross-sectional visualization of ocular structures. The increasing integration of artificial intelligence with OCT is creating new possibilities for automated image interpretation, segmentation, disease detection, and quantitative assessment.
This session will focus on AI-based analysis of OCT images across retinal, macular, optic nerve, and glaucoma-related conditions. Emerging computational methods will be discussed for identifying subtle structural changes, segmenting retinal layers, detecting pathological features, and supporting longitudinal assessment of disease.
The scientific discussion will also consider the challenges associated with image quality, segmentation accuracy, dataset variability, model generalizability, and clinical implementation. Researchers and clinicians will explore how AI-enhanced OCT analysis can contribute to more efficient imaging workflows and improved clinical decision support.