Retinalscans: A non-invasive, inexpensive method to track humanaging elife
Biological age, distinct from an individual's chronological age, has been studied extensively through predictive aging clocks. However, these clocks have limited accuracy in short time-scales. Here we trained deep learning models on fundus images from the EyePACS dataset to predict individuals' chronological age. Our retinal aging clocking, 'eyeAge', predicted chronological age more accurately than other aging clocks .
A subset of EyePACS data is freely available online . To enquire about access to the full EyePACS dataset, researchers should contact Jorge Cuadros . Proposals and agreements are assessed internally at EyePACS and may be subject to ethics approvals. The UKB data are available for approved projects through the UK Biobank Access Management System .
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