Artificial intelligence-enhanced electrocardiography to predict regurgitant valvular heart diseases: an international study
Open accessAbstract
Background and Aims: Valvular heart disease (VHD) is a significant source of morbidity and mortality, though early intervention can improve outcomes. This study aims to develop artificial intelligence-enhanced electrocardiography (AI-ECG) models to diagnose and predict future moderate or severe regurgitant VHDs (rVHDs), including mitral regurgitation (MR), tricuspid regurgitation (TR), and aortic regurgitation (AR). Methods: The AI-ECG models were developed in a data set of 988 618 ECG and transthoracic echocardiogram pairs from 400 882 patients from Zhongshan Hospital, Shanghai, China. The AI-ECG models used a residual convolutional neural network with a discrete-time survival loss function. External evaluation was performed in outpatients from a secondary care data set from Beth Israel Deaconess Medical Center, Boston, USA, consisting of 34 214 patients with linked echocardiography. Results: In the internal test set, the AI-ECG models accurately predicted future significant MR [C-index 0.774, 95% confidence interval (CI) 0.753–0.792], AR (0.691, 95% CI 0.657–0.720), and TR (0.793, 95% CI 0.777–0.808). In age- and sex-adjusted Cox models, the highest risk quartile had a hazard ratio (HR) of 7.6 (95% CI 5.8–9.9, P < .0001) for risk of future significant MR, compared with the lowest risk quartile. For future AR and TR, the equivalent HRs were 3.8 (95% CI 2.7–5.5) and 9.9 (95% CI 7.5–13.0), respectively. These findings were confirmed in the transnational external test set. Imaging association analyses demonstrated AI-ECG predictions were associated with subclinical chamber remodelling. Conclusions: This study developed AI-ECG models to diagnose and predict rVHDs and validated the models in a transnational and ethnically distinct cohort. The AI-ECG models could be utilized to guide surveillance echocardiography in patients at risk of future rVHDs, to facilitate early detection and intervention.
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Valvular and composite SHD AI-ECG models are promising for referral enrichment.
“Valvular and composite SHD models are promising for referral enrichment”
From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [24]
The Discussion says the valvular AI-ECG models could guide surveillance echocardiography for patients at risk of regurgitant valve disease, supporting the proposed referral use. The paper does not evaluate a composite structural heart disease (SHD) model, so that part of the claim is unsupported. Page numbers are unavailable in our copy. (LLM, conf=0.86)
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- Authors
- Liang, Y., Sau, A., Zeidaabadi, B., Barker, J., Patlatzoglou, K., Pastika, L., Sieliwonczyk, E., Whinnett, Z., Peters, N. S., Yu, Z., Liu, X., Wang, S., Lu, H., Kramer, D. B., Waks, J. W., Su, Y., Ge, J., & Ng, F. S.
- Affiliations
- Fudan University (CN)
- Imperial College Healthcare NHS Trust (GB)
- Hammersmith Hospital (GB)
- National Heart and Lung Institute, Imperial College London, Hammersmith Hospital , Du Cane Road, London W12 0NN ,
- Shanghai Medical College of Fudan University (CN)
- Medtronic (China) (CN)
- Beth Israel Deaconess Medical Center (US)
- Corresponding author
- Sau, Arunashis
- Ge, Junbo
- Ng, Fu Siong
- ORCID
- Venue
- European Heart Journal
- Publisher
- Oxford University Press (OUP)
- Published
- 2025-07-16
- Volume
- 46
- Issue
- 44
- Pages
- 4823-4837
- ISSN
- 0195-668X (ISSN-L)
- 1522-9645
- PMC
- PMC12634117
- OpenAlex
- W4412488540
- Cited by
- 26
- References
- 43
- Citations by year
- 2026: 16 · 2025: 10
- FWCI
- 18.16
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- European Heart Journal (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Spiral (Imperial College London) (submittedVersion, cc-by)
- Funders
- Shanghai Municipal Key Clinical Specialty (shslczdzk01701)
- Fudan University AI4S Project (FudanX24AI056)
- British Heart Foundation (FS/CRTF/21/24183)
- NIHR Academic Clinical Lectureship
- Medical Research Council (MR/Y000803/1)
- BHF (RG/F/22/110078, RE/18/4/34215)
- EJP RD Research Mobility Fellowship
- National Institute for Health Research Imperial Biomedical Research Centre
- Imperial’s BHF Centre (RE/18/4/34215, RE/24/130023)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiac Valve Diseases and Treatments
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- mitral regurgitation, tricuspid regurgitation, aortic regurgitation, residual convolutional neural network, risk prediction, transthoracic echocardiography, early detection
- MeSH terms
- Aged, Aortic Valve Insufficiency, Artificial Intelligence, Echocardiography, Electrocardiography, Female, Heart Valve Diseases, Humans, Male, Middle Aged, Mitral Valve Insufficiency, Tricuspid Valve Insufficiency
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