Zhu and co-workers aimed to develop a deep learning (DL) model that predicts age from fundus images (retinal age) and to investigate the association between retinal age gap (retinal age predicted by deep learning model minus chronological age) and mortality risk. A total of 80 169 fundus images taken from 46 969 participants with reasonable quality were included in this study. Zhu and co-workers concluded that the deep learning model can detect footprints of aging in fundus images and predict age with high accuracy. They demonstrated that each one-year increase in retinal age gap (retinal age-chronological age) was significantly associated with a 2% increase in mortality risk.
Zhu Z, Shi D, Guankai P, et al. Retinal age gap as a predictive biomarker for mortality risk. Br J Ophthalmol 2022. doi: 10.1136/bjophthalmol-2021-319807. https://pubmed.ncbi.nlm.nih.gov/35042683/