AI-DRIVEN OCULOMICS: RETINAL IMAGING BIOMARKERS FOR SYSTEMIC DISEASE - FROM CARDIOVASCULAR RISK TO NEURODEGENERATION AND FOUNDATION MODELS

Introduction and aim: The retina offers a unique, non-invasive window into systemic vascular and neurological health. Deep-learning algorithms applied to retinal photographs and optical coherence tomography (OCT) can predict cardiovascular events, detect chronic kidney disease, estimate biological age, and identify neurodegenerative diseases - a field termed oculomics. This review synthesizes current evidence on AI-driven oculomics across cardiovascular, cardio-renal-metabolic, neurodegenerative, and aging domains. Materials and methods: A narrative review was conducted in PubMed, Scopus, and Google Scholar for studies published 2018-2026. Original research, validation studies, and major reviews on AI-based prediction of systemic disease from retinal imaging were included. Results: Deep-learning models trained on fundus photographs predict major adverse cardiovascular events and ten-year atherosclerotic cardiovascular disease risk with areas under the receiver operating characteristic curve (AUROCs) of 0.70-0.89. Chronic kidney disease detection achieves AUROCs of 0.85-0.94. Alzheimer’s disease classifiers reach AUROCs of 0.93 in development cohorts and 0.73-0.91 in external validation, while Parkinson’s disease detection achieves AUROCs of 0.77-0.92. Retinal age-gap independently predicts mortality. Self-supervised foundation models (RETFound, VisionFM, EyeFound, RETFound-Green, MIRAGE, V-JEPA) show state-of-the-art performance, with volumetric OCT (V-JEPA) reaching AUROCs of 0.94. Conclusions: AI-driven oculomics is a rapidly maturing field with strong translational potential. Despite encouraging accuracies, prospective validation, algorithmic bias, regulatory approval, and clinical integration remain key challenges. Future work should prioritize multi-ethnic cohort studies, cost-effectiveness analyses, and regulatory pathways.