FOUR-DIMENSIONAL (4D) motion analysis of the left ventricle has unveiled six new motion phenogroups for determining cardiovascular disease outcomes and genetic risk, according to researchers from Imperial College London, United Kingdom.
Conventional cardiac assessment relies heavily on global volumetric measurements, which often fail to capture more subtle or early abnormalities in heart function. Now, this large-scale analysis has demonstrated the potential for motion analysis to identify distinct cardiac outcomes.
AI Analysis of 4D Point Cloud Models
Participant samples were sourced from the UK Biobank, which recruited 500,000 participants aged 40 to 69 years, between 2006 and 2010.
Researchers used image processing artificial intelligence (computer vision) to analyse cardiac motion traits in over 20,000 eligible participant samples of the left ventricle. The image samples were converted into 4D point cloud models, capturing the full shape of the left ventricle and how the shape changed throughout the cardiac cycle.
Six clusters (phenogroups) of motion traits were identified by disease prevalence, future cardiac events, and polygenic risk scores. The researchers compared these clusters with models of the population average (control) to define the diversity of motion patterns.
Six Phenogroups of Cardiac Motion
Phenogroups 1 and 2 (PG1 and PG2) were determined as the lowest risk groups, with the least risk for obesity, diabetes and hypertension, suggesting that healthy individuals with low risk of adverse outcomes could also be identified through motion analysis.
While PG3 showed no strong distinguishing risk factors, PG4 was most closely associated with cardiometabolic disease, such as diabetic cardiomyopathy, demonstrating that motion analysis could also identify metabolic risk.
PG5 and PG6 showed the most prevalence and incidence of heart disease, as well as the highest polygenic risk scores. While PG6 displayed a notable prevalence of incident cardiac arrest, PG5 was linked more to hypertension and dilated cardiomyopathy. PG6 was also the only cluster that showed higher incident myocardial infarction and cardiac disease, suggesting motion traits may also be predictive of fatal outcomes
Study Limitations and Clinical Implications
Despite its promise, the study has limitations. The model focuses only on the left ventricle, excluding other cardiac chambers, which challenges the wider applicability of the phenogroups.
The UK Biobank samples were predominantly comprised of participants with European ancestries, and older adults and persons living in socioeconomically deprived areas were under-represented. Further research would be needed to understand motion analysis’ capabilities across diverse populations and social groups.
Nonetheless, if validated in clinical settings, 4D cardiac motion analysis has the potential to redefine the screening and management of heart disease, furthering advancements within personalised cardiac care.
Reference
Schiratti PR et al. Four-dimensional left ventricular motion clustering reveals cardiovascular phenotypes at population scale. Sci Rep. 2026; DOI: 10.1038/s41598-026-56151-y
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