AI-driven multimodal retinal imaging for early detection and risk stratification of vascular and neurodegenerative diseases.

Research overview

The text below is the source article’s original abstract under a verified reuse license. cc by

Systemic vascular and neurodegenerative disorders are important causes of disability and death worldwide, mainly because of the late stage of diagnosis and the high cost of current screening tools. Artificial intelligence (AI) and multimodal retinal imaging offer a non-invasive and viable approach for early risk stratification and longitudinal monitoring. This review highlights how changes in the retinal vasculature and nerve layers are markers of underlying pathophysiologies related to cardiovascular, metabolic, and neurological disorders. It gives an account of the critical retinal imaging modalities, such as fundus photography, optical coherence tomography (OCT), OCT angiography (OCTA), and more recently developed metabolic-sensitive imaging modalities, and how current AI approaches, such as deep learning, self-supervised learning, and multimodal fusion, can be leveraged for better risk stratification and decision support. Evidence from hypertension, stroke, coronary artery disease, diabetic complications, Alzheimer's disease, Parkinson's disease, multiple sclerosis, and cognitive impairment shows the potential for the retina to serve as a scalable biomarker for systemic health. However, there are still hurdles to be cleared, such as multicenter validation, prospective clinical trials, data fusion, and regulatory frameworks. In conclusion, AI-assisted retinal analysis may make way for early screening, better prevention, and more accessible precision healthcare.

Related topics