23 Jun 2026 Jess Nicholson American Medical Journal
OPTHALMIC imaging features linked to cardiovascular and neurodegenerative disease phenotypes, following an integrated multi-omics analysis combining artificial intelligence (AI)-derived imaging embeddings with physiological, radiomic, metabolomic and genomic datasets.
AI-Derived Retinal Features Reveal Links to Systemic Disease Risk
A new research article leveraged UK BioBank data to investigate whether retinal features extracted from optical coherence tomography (OCT) and colour fundus photography (CFP) could serve as high-dimensional biomarkers of systemic disease. Findings indicated that AI-derived retinal representations were associated with both prevalent and incident cardiometabolic and neurodegenerative outcomes, including ischaemic heart disease, cerebrovascular disease, heart failure, Parkinson’s disease and dementia.
Retinal imaging was processed using a deep-learning based adversarial autoencoder framework, generating 256-dimensional latent embeddings from OCT and CFP data. These embeddings were evaluated against longitudinal disease outcome and multi-layered biological datasets.
Retinal Signatures Reflect Vascular, Metabolic and Neurological Changes
Saliency mapping identified cardiovascular associates were predominantly localised to the choroid and retinal vascular network, while neurodegenerative associations were more strongly linked to the optic nerve head and neurosensory retinal layers.
Metabolomic analysis also found links between retinal features and lipid metabolism, suggesting that changes seen in the eye may reflect shared metabolic processes that contribute to both cardiovascular disease and neurodegeneration.
In addition, analysis of brain imaging data showed that retinal features were associated with differences in brain structure, including regional brain volumes and measures of white matter organisation.
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