Three-dimensional motion-capture of the heart uncovers signatures of human health and disease

Adaptations of the motion dynamics of the heart are key to understanding pathophysiological mechanisms and transitions from health to disease. Conventional volumetric assessments of the heart using imaging represent mainly aggregate global features of function that are poorly discriminating. Here we aimed to optimise understanding of how the heart is affected by cardiovascular risk factors through efficient representations of motion trajectories. We use computer vision to survey three-dimensional cardiac motion traits using densely sampled point clouds of the heart in over 20,000 participants of UK Biobank. We developed a computational framework for dimensionality reduction of spatiotemporal information to derive a human-interpretable signature summarising variation in complex patterns of motion. We found six phenogroups representing a novel classification of heterogenous motion phenotypes with differential enrichment of cardiovascular outcomes and genetic risk. Low dimensional representations of motion are visualised as a simple spatial signature capturing deviation from an average state. Discovering compact cardiac motion signatures of health and disease enables efficient classification of patient risk and predisposing polygenic factors.