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This article presents a study that used computer vision techniques to analyse cardiac magnetic resonance (CMR) imaging in 39,559 participants of the UK Biobank to extract image-derived phenotypes of the three-dimensional (3D) geometry and motion of the heart as well as dynamic assessment of vascular function. These image-derived traits were used to train supervised machine learning algorithms to predict participants’ ages and derive a cardiovascular age-delta for each individual, quantifying the deviation in years from healthy ageing. The study also found ageing-associated loci containing genes known to be associated with tissue elasticity, myocardial contractility, inflammatory regulation and immune response to apoptosis.