Year: 2026
Type: preprint
Abstract: Cardiac imaging enables quantitative assessment of cardiac structure and function but remains constrained by cost, infrastructure and specialist expertise. In contrast, electrocardiogram (ECG) is widely accessible yet underexploited, despite encoding latent information about cardiac physiology. Here we introduce visionECG , a conditional flow matching framework that learns a probabilistic mapping between two biological distributions—the space of cardiac electrical signals and the space of cardiac geometries. Using 71,132 paired ECG and cardiac mesh sequence datasets from the UK Biobank, with external assessment in 5,000 patients with ECG-echocardiogram pairs, the model reconstructs quantitatively accurate spatiotemporal representations of the left ventricle using ECG inputs and basic demographic information alone. These reconstructions enable discrimination of structural abnormalities and disease labels, provide visualisations of functional abnormalities, and support flexible quantification of both global and regional parameters. By reframing the ECG as a generative source of patient-specific left ventricular geometry and motion, this work establishes a scalable framework for translating low-dimensional signals into high-dimensional, physiologically grounded structured representations.
Source: medRxiv
Authors: Jin Zheng, Soodeh Kalaie, Qiang Ma, Qingjie Meng, Khaled Rjoob, Parisa Gifani, Lei Hu, Nigar Babazade, Mattia Corianò, Wei Zhong, Majid Vafaeezadeh, Shamin Tahasildar, Nirmal Vadgama, Deva S Senevirathne, Ainkaran Santhirasekaram, Kathryn A McGurk, Lara Curran, Ying He, Long Chen, Yuanhan Mo, Ling Huang, Mengyun Qiao, Yuan Huang, Wenjia Bai, Declan P O’Regan
Language: English
Topic: ECG Monitoring and Analysis
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: green
Awards: G1138586538, Discovery of disease mechanisms and therapies through imaging AI, Population-level dynamic shape modelling of the whole heart, Rare variant and phenotype analyses of adult cardiac trabecular morphology in the UK Biobank, Curing genetic heart muscle disease (CureHeart), G402563391, G4476662549, G7562238089, Networks of Cardiovascular Digital Twins (CVD-Net), G843167766
OpenAlex ID: W7208739008