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Article

Developing cardiac digital twin populations powered by machine learning provides electrophysiological insights in conduction and repolarization.

Nature cardiovascular research - 1 May 2025

Qian Shuang, Ugurlu Devran, Fairweather Elliot, Toso Laura Dal, Deng Yu, Strocchi Marina, Cicci Ludovica, Jones Richard E, Zaidi Hassan, Prasad Sanjay, Halliday Brian P, Hammersley Daniel, Liu Xingchi, Plank Gernot, Vigmond Edward, Razavi Reza, Young Alistair, Lamata Pablo, Bishop Martin, Niederer Steven

Abstract excerpt

Large-cohort imaging and diagnostic studies often assess cardiac function but overlook underlying biological mechanisms. Cardiac digital twins (CDTs) are personalized physics-constrained and physiology-constrained in silico representations, uncovering multi-scale insights tied to these mechanisms. In this study, we constructed 3,461 CDTs from the UK Biobank and another 359 from an ischemic heart disease (IHD)...

Read the complete abstract on PubMed

Topics

Humans
Female
Male
Machine Learning
Electrocardiography

Methodology Data sources Editorial policy Privacy Delete account & data About Contact Helena Bioinformatics

Middle Aged

Heart Conduction System

Aged

Sex Factors

Heart Rate

Action Potentials

Adult

Magnetic Resonance Imaging

Predictive Value of Tests

Age Factors

Phenotype

Myocardial Ischemia

Obesity

Source

Open in PubMed Open full text

Identifiers

PMID: 40379795
DOI: 10.1038/s44161-025-00650-0
PMCID: PMC12084159

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