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We reported the development and validation of deep-learning models for the automated measurement of retinal vessel calibre in retinal photographs, by using diverse multi-ethnic multi-country datasets amounting to more than 70,000 images, including data from UK Biobank. Retinal vessel calibres measured by the models and by expert human graders showed high agreement, with overall intraclass correlation coefficients between 0.82 and 0.95. The models performed comparably to or better than expert graders in associations of measurements of retinal vessel calibre with cardiovascular disease (CVD) risk factors, including blood pressure, body mass index, total cholesterol and glycated-haemoglobin levels. In retrospectively measured prospective datasets from a population-based study, baseline measurements by the deep-learning system were associated with incident CVD. Our findings motivate the development of clinically applicable explainable end-to-end deep-learning systems for the prediction of CVD on the basis of features of retinal vessels in retinal photographs.