Artificial intelligence-enhanced electrocardiography models for the diagnosis and prediction of future regurgitant valvular heart diseases: an international multi-center study
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Author(s)
Type
Journal Article
Abstract
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 electrocardiogram (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 dataset 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 dataset 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%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 < 0.0001) for risk of future significant MR, compared to 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 remodeling.
Conclusions: This study developed AI-ECG models to diagnose and predict future rVHDs and validated the models in a transnational and ethnically distinct cohort. AI-ECG could be utilized to guide surveillance echocardiography in patients at risk of future rVHDs, to facilitate early detection and intervention.
REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT06475157.
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 electrocardiogram (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 dataset 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 dataset 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%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 < 0.0001) for risk of future significant MR, compared to 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 remodeling.
Conclusions: This study developed AI-ECG models to diagnose and predict future rVHDs and validated the models in a transnational and ethnically distinct cohort. AI-ECG could be utilized to guide surveillance echocardiography in patients at risk of future rVHDs, to facilitate early detection and intervention.
REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT06475157.
Date Issued
2025-11-21
Date Acceptance
2025-06-10
Citation
European Heart Journal, 2025, 46 (44), pp.4823-4837
ISSN
0195-668X
Publisher
Oxford University Press
Start Page
4823
End Page
4837
Journal / Book Title
European Heart Journal
Volume
46
Issue
44
Copyright Statement
© The Author(s) 2025. Published by Oxford University Press on behalf of the European Society of Cardiology. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
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Publication Status
Published
Date Publish Online
2025-07-16