Implementing Predictive Models in Artificial Intelligence through OCT Biomarkers for Age-Related Macular Degeneration

Abstract

Artificial intelligence (AI) represents a growing and promising branch of computer science that is expanding the horizon of prediction, screening, and disease monitoring. The use of multimodal imaging in retinal diseases is particularly advantageous to valorize the integration of machine learning and deep learning for early diagnosis, prediction, and management of retinal disorders. In age-related macular degeneration (AMD) beyond its diagnosis and characterization, the prediction of AMD high-risk phenotypes evolving into late forms remains a critical point. The main multimodal imaging modalities adopted included color fundus photography, fundus autofluorescence, and optical coherence tomography (OCT), which represents undoubtful advantages over other methods. OCT features identified as predictors of late AMD include the morphometric evaluation of retinal layers, drusen volume and topographic distribution, reticular pseudodrusen, and hyperreflective foci quantification. The present narrative review proposes to analyze the current evidence on AI models and biomarkers identified to predict disease progression with particular attention to OCT-based features and to highlight potential perspectives for future research.