Previous work showed the potential of retinal images for risk assessment for some diseases, but it remains unclear whether this potential extends to a broader range of human diseases.
We aimed to systematically assess the extent to which retinal fundus photographs can predict incident disease onset across the human phenome, and to benchmark their added value beyond readily available patient characteristics.
In this modelling study using data from population-based cohorts, we extended a retinal foundation model (RETFound) to systematically explore the predictive potential of retinal images as a screening strategy for disease onset across 752 incident diseases in 61,256 individuals from the UK Biobank cohort.
We showed improved discriminative performance compared with readily available patient characteristics for 306 (41%) of the 752 investigated disease endpoints, including 280 outside of ophthalmology.
Predictive improvements were attributable to retinal vascularisation patterns and less obvious features, such as eye colour or lens morphology.
We present one of the first comprehensive evaluations of predictive information derived from retinal fundus photographs, illustrating the potential and limitations of readily accessible and low-cost retinal images for risk assessment across common and rare diseases.
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