Daijiworld Media Network – London

London, Jun 18: Researchers from Imperial College London have developed an advanced artificial intelligence-based method that can identify six distinct heart motion patterns linked to cardiovascular disease risk, offering a potential breakthrough in the early detection and personalised treatment of heart conditions.

The study focused on four-dimensional (4D) motion analysis of the left ventricle, the heart's main pumping chamber. Unlike conventional cardiac assessments that rely largely on overall heart size and volume measurements, the new technique examines subtle changes in heart movement throughout the cardiac cycle.

Using data from more than 20,000 participants enrolled in the UK Biobank project, researchers employed computer vision and artificial intelligence tools to analyse cardiac motion traits. The imaging data were transformed into 4D point-cloud models, allowing scientists to study the shape of the left ventricle and how it changes during each heartbeat.

The analysis identified six distinct cardiac motion groups, or "phenogroups", each associated with different levels of cardiovascular risk, disease prevalence and genetic susceptibility.

According to the researchers, Phenogroups 1 and 2 represented the lowest-risk categories, with participants showing lower rates of obesity, diabetes and hypertension. These groups were considered to reflect healthier heart function and a reduced likelihood of future cardiac complications.

Phenogroup 3 showed no strong distinguishing risk factors, while Phenogroup 4 was found to be closely associated with cardiometabolic conditions, including diabetic cardiomyopathy, highlighting the method's ability to detect metabolic-related heart abnormalities.

The highest-risk categories were Phenogroups 5 and 6, which exhibited greater prevalence of heart disease and higher polygenic risk scores. Phenogroup 5 was strongly linked to hypertension and dilated cardiomyopathy, whereas Phenogroup 6 showed a notable association with cardiac arrest, heart attacks and other serious cardiovascular events.

Researchers said the findings suggest that heart motion patterns could serve as powerful indicators of future cardiac outcomes, potentially enabling earlier intervention and more targeted treatment strategies.

However, the study also highlighted certain limitations. The analysis focused exclusively on the left ventricle and did not assess other chambers of the heart. In addition, most participants in the UK Biobank were of European ancestry, limiting the immediate applicability of the findings to more diverse populations.

The researchers noted that further studies are needed to validate the approach across different ethnic and demographic groups before it can be widely adopted in clinical practice.

If confirmed through future research, the technology could significantly enhance cardiovascular screening programmes and contribute to more personalised approaches to heart disease prevention and management.