Four-dimensional left ventricular motion clustering reveals cardiovascular phenotypes at population scale
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
Characterisation of the motion dynamics of the left ventricle is 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 present a novel approach to quantify and visualise how the left ventricle is affected by cardiovascular risk factors through efficient representations of motion trajectories. We use computer vision to survey four-dimensional cardiac motion traits using densely sampled point clouds of the left ventricle 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 from dynamic point clouds enables efficient classification of patient risk and predisposing polygenic factors.
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 from dynamic point clouds enables efficient classification of patient risk and predisposing polygenic factors.
Date Issued
2026-08-26
Date Acceptance
2026-05-29
Citation
Scientific Reports, 2026, 16
ISSN
2045-2322
Publisher
Nature Portfolio
Journal / Book Title
Scientific Reports
Volume
16
Copyright Statement
© The Author(s) 2026. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
License URL
Identifier
10.1038/s41598-026-56151-y
Publication Status
Published
Article Number
26725
Date Publish Online
2026-06-11