Machine learning and deep learning are becoming important components of modern ophthalmic research, offering sophisticated approaches for analyzing complex clinical and imaging datasets. These computational methods can identify patterns within large datasets and support disease detection, classification, risk assessment, and outcome prediction. Their growing application is creating new opportunities for advancing both clinical research and patient care.
This session will explore contemporary machine-learning methodologies and deep-learning architectures used in ophthalmology and vision science. Scientific discussions will consider applications involving retinal imaging, optical coherence tomography, fundus photography, visual fields, clinical records, and multimodal datasets, highlighting how computational models can support diagnostic and prognostic decision-making.
Particular emphasis will be placed on model development, external validation, generalizability, dataset diversity, and algorithmic bias. Researchers and clinicians will discuss the transition of machine-learning technologies from experimental research to clinically meaningful applications while considering transparency, reproducibility, and responsible implementation.