Aim 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 DL model minus chronological age) and mortality risk. Methods A total of 80 169 fundus images taken from 46 969 participants in the UK Biobank with reasonable quality were included in this study. Of these, 19 200 fundus images from 11 052 participants without prior medical history at the baseline examination were used to train and validate the DL model for age prediction using fivefold cross-validation. A total of 35 913 of the remaining 35 917 participants had available mortality data and were used to investigate the association between retinal age gap and mortality. Results The DL model achieved a strong correlation of 0.81 (p<0·001) between retinal age and chronological age, and an overall mean absolute error of 3.55 years. Cox regression models showed that each 1 year increase in the retinal age gap was associated with a 2% increase in risk of all-cause mortality (hazard ratio (HR)=1.02, 95% CI 1.00 to 1.03, p=0.020) and a 3% increase in risk of cause-specific mortality attributable to non-cardiovascular and non-cancer disease (HR=1.03, 95% CI 1.00 to 1.05, p=0.041) after multivariable adjustments. No significant association was identified between retinal age gap and cardiovascular- or cancer-related mortality. Conclusions Our findings indicate that retinal age gap might be a potential biomarker of ageing that is closely related to risk of mortality, implying the potential of retinal image as a screening tool for risk stratification and delivery of tailored interventions. Data are available in a public, open access repository.

Congenital heart disease (CHD), the most common congenital birth defect, has long been known as one of the main causes of infant death during the first year of life.1 More than one million of the world's approximately 135 million newborns are born each year with CHD.21 Over the last century, cardiac surgery has been an effective approach to tackling CHD; its remarkable advance has decreased the mortality rate of newborns with CHD.10 However, that lower mortality rate is mostly observed in developed countries rather than developing ones. Surgical treatment of CHD requires highly skilled surgeons along with complex infrastructures and equipment. While developed countries have perfected their treatment of CHD for more than 50 years, developing countries are still in the early stages. It is estimated that the number of congenital cardiac surgeons needs to increase by 1,250 times to satisfy only the basic needs of CHD treatment worldwide,16 and most of those surgeons reside in developed countries. As a result, the mortality rate in developing countries is currently at 20%, strikingly higher than the 3% to 7% in developed countries,16 not to mention the fact that mortality rates in developing countries are likely underreported due to the lack of proper diagnosis. Remote surgery has been an active field for decades, enabling experienced surgeons to remotely instruct robots (telerobotics) or guide less-experienced surgeons (surgical telementoring).8