Unlocking the diagnostic potential of electrocardiograms through information transfer from cardiac magnetic resonance imaging
Open accessAbstract
Cardiovascular diseases (CVD) can be diagnosed using various diagnostic modalities. The electrocardiogram (ECG) is a cost-effective and widely available diagnostic aid that provides functional information of the heart. However, its ability to classify and spatially localise CVD is limited. In contrast, cardiac magnetic resonance (CMR) imaging provides detailed structural information of the heart and thus enables evidence-based diagnosis of CVD, but long scan times and high costs limit its use in clinical routine. In this work, we present a deep learning strategy for cost-effective and comprehensive cardiac screening solely from ECG. Our approach combines multimodal contrastive learning with masked data modelling to transfer domain-specific information from CMR imaging to ECG representations. In extensive experiments using data from 40,044 UK Biobank subjects, we demonstrate the utility and generalisability of our method for subject-specific risk prediction of CVD and the prediction of cardiac phenotypes using only ECG data. Specifically, our novel multimodal pre-training paradigm improves performance by up to 12.19% for risk prediction and 27.59% for phenotype prediction. In a qualitative analysis, we demonstrate that our learned ECG representations incorporate information from CMR image regions of interest. Our entire pipeline is publicly available at https://github.com/oetu/MMCL-ECG-CMR.
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Convincing prospective evidence that ECG-centred multimodal approaches can safely support gatekeeper-type decision making is still sparse.
“At present, however, direct SHD-specific evidence for ECG-centred multimodal screening remains limited, and convincing prospective evidence that such approaches can safely support gatekeeper-type decision making is still sparse.69,70”
From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [69]
The Dataset and pre-processing section (p. 5) describes a UK Biobank training and test dataset, while Prediction targets (p. 5) lists CAD, AF and diabetes rather than a direct SHD-specific screening evaluation. The limitations (p. 10) also note that the cohort was mainly healthy. This supports the narrower point that this study does not establish prospective gatekeeper safety, but the paper does not assess whether such evidence is sparse across the field. (LLM, conf=0.76)
Experimental — accuracy not yet measuredDirect SHD-specific evidence for ECG-centred multimodal screening remains limited.
“At present, however, direct SHD-specific evidence for ECG-centred multimodal screening remains limited, and convincing prospective evidence that such approaches can safely support gatekeeper-type decision making is still sparse.69,70”
From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [69]
The limitations discussion (p. 10) says this study included mainly healthy subjects and calls for investigation with more balanced data. It does not assess whether SHD-specific evidence for ECG-centred multimodal screening is limited across the field, or whether prospective evidence supports safe gatekeeper decisions. Searches for SHD-specific screening, prospective validation, and gatekeeper decisions found no passage addressing those claims. (LLM, conf=0.72)
Experimental — accuracy not yet measured
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“69,70”
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Self-citationCenikj, N., Turgut, Ö., Müller, A., Steger, A., Kehrer, J., Brugger, M., Rueckert, D., Martens, E., & Müller, P. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-38091-3_15
Self-citationArtificial Intelligence in Cardiac Remodeling Assessment: From Models to Clinical Integration
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Reconstructing synthetic hearts from ECG using flow matchingPreprint
Zheng, J., Kalaie, S., Ma, Q., Meng, Q., Rjoob, K. W. Y., Gifani, P., Hu, L., Babazade, N., Corianò, M., Zhong, W., et al. (2026) · medRxiv · doi:10.64898/2026.09.01.26360987
Geometry-Aware Self-supervised Learning for Whole-Heart 3D+t Representation
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- Authors
- Turgut, Ö., Müller, P., Hager, P., Shit, S., Starck, S., Menten, M. J., Martens, E., & Rueckert, D.
- Affiliations
- TUM Klinikum (DE)
- University of Zurich (CH)
- Munich Center for Machine Learning (DE)
- ORCID
- Venue
- Medical Image Analysis
- Publisher
- Elsevier BV
- Published
- 2025-04
- Volume
- 101
- Pages
- 103451
- ISSN
- 1361-8415 (ISSN-L)
- OpenAlex
- W4406069341
- Cited by
- 22
- References
- 74
- Citations by year
- 2026: 17 · 2025: 4 · 2024: 1
- FWCI
- 14.67
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Medical Image Analysis (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiac electrophysiology and arrhythmias
- Keywords
- electrocardiogram, cardiac magnetic resonance imaging, cardiovascular risk prediction, multimodal contrastive learning, cardiac screening, UK Biobank
- MeSH terms
- Deep Learning, Cardiovascular Diseases, Electrocardiography, Female, Humans, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Male, Middle Aged
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