@article{Schiratti:2026:10.1038/s41598-026-56151-y,
author = {Schiratti, P-R and Kalaie, S and Zheng, J and Inglese, P and McGurk, K and Zheng, S and Ware, J and Bai, W and O'Regan, D},
doi = {10.1038/s41598-026-56151-y},
journal = {Scientific Reports},
title = {Four-dimensional left ventricular motion clustering reveals cardiovascular phenotypes at population scale},
url = {http://dx.doi.org/10.1038/s41598-026-56151-y},
volume = {16},
year = {2026}
}

Download

TY  - JOUR
AB  - 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 dimensionalityreduction of spatiotemporal information to derive a human-interpretable signature summarising variation in complexpatterns 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.
AU  - Schiratti,P-R
AU  - Kalaie,S
AU  - Zheng,J
AU  - Inglese,P
AU  - McGurk,K
AU  - Zheng,S
AU  - Ware,J
AU  - Bai,W
AU  - O'Regan,D
DO  - 10.1038/s41598-026-56151-y
PY  - 2026///
SN  - 2045-2322
TI  - Four-dimensional left ventricular motion clustering reveals cardiovascular phenotypes at population scale
T2  - Scientific Reports
UR  - http://dx.doi.org/10.1038/s41598-026-56151-y
VL  - 16
ER  - 

Download