Training and validation of a 12-lead ECG-based deep-learning model for myocardial infarction subtypes

Abstract A convolutional neural network was developed to detect acute myocardial infarction (AMI) subtypes from digital 12-lead ECGs. Trained on 173,396 hospitalized patients’ ECGs, the model underwent fine-tuning and internal validation in 7591 patients and external validation in 4370 patients with suspected AMI. Both prospective validation studies employed central diagnostic adjudication. For non-ST-segment elevation MI (NSTEMI), the model achieved an area under the receiver operating characteristic curve (AUROC) of 0.81 [95%-confidence interval (CI) 0.79–0.83], with NSTEMI type 1 at 0.82 [0.80–0.84] versus type 2 at 0.75 [0.71–0.79]. Performance was superior in younger patients without prior heart disease. For ST-segment elevation MI (STEMI), the model outperformed physician interpretation (AUROC 0.96 [95%-CI 0.94–0.98] vs. 0.89 [0.85–0.93], p < 0.001). Occlusion MI detection reached AUROC 0.91 [95%-CI 0.89–0.93], though NSTEMI-OMI cases were frequently missed. Overall calibration was good for all MI subtypes. This ECG-based deep learning model demonstrates good discrimination and calibration across AMI subtypes, indicating potential clinical utility for rapid risk stratification and early cardiology intervention.

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Publication Details

Journal
npj Digital Medicine
Published
2026-09-14
DOI
https://doi.org/10.1038/s41746-026-03204-1
Primary Topic
ECG Monitoring and Analysis
Type
article
Field-Weighted Citation Impact
0.00

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article

Training and validation of a 12-lead ECG-based deep-learning model for myocardial infarction subtypes

Ivo Strebel, Thorald Stolte, Tiffany Péquignot, Koray Durak et al.
npj Digital Medicine
ECG Monitoring and Analysis
article

Training and validation of a 12-lead ECG-based deep-learning model for myocardial infarction subtypes

Ivo Strebel, Thorald Stolte, Tiffany Péquignot, Koray Durak, Pedro López‐Ayala, Michael Christ, Felix Mahfoud, Dominik Lemm, Sven Knecht, Jasper Boeddinghaus, Christian Müller, Lea Kirsten, Wayne Zeng, Emel Kaplan, Paolo Bima, Tobias Zimmermann, Arnaud Tanguy Champetier, Luca Koechlin, Volker Roth, Kunalish Kulendra, Stefan Osswald, Karin Wildi
article en

Abstract

Abstract A convolutional neural network was developed to detect acute myocardial infarction (AMI) subtypes from digital 12-lead ECGs. Trained on 173,396 hospitalized patients’ ECGs, the model underwent fine-tuning and internal validation in 7591 patients and external validation in 4370 patients with suspected AMI. Both prospective validation studies employed central diagnostic adjudication. For non-ST-segment elevation MI (NSTEMI), the model achieved an area under the receiver operating characteristic curve (AUROC) of 0.81 [95%-confidence interval (CI) 0.79–0.83], with NSTEMI type 1 at 0.82 [0.80–0.84] versus type 2 at 0.75 [0.71–0.79]. Performance was superior in younger patients without prior heart disease. For ST-segment elevation MI (STEMI), the model outperformed physician interpretation (AUROC 0.96 [95%-CI 0.94–0.98] vs. 0.89 [0.85–0.93], p < 0.001). Occlusion MI detection reached AUROC 0.91 [95%-CI 0.89–0.93], though NSTEMI-OMI cases were frequently missed. Overall calibration was good for all MI subtypes. This ECG-based deep learning model demonstrates good discrimination and calibration across AMI subtypes, indicating potential clinical utility for rapid risk stratification and early cardiology intervention.

npj Digital Medicine
University of Lucerne (CH), University of Basel (CH), University Hospital Bonn (DE), University Hospital of Basel (CH), University Hospital of Zurich (CH), Gradient (United States) (US), GrAT - Center for Appropriate Technology (AT), Zero Emissions Resource Organisation (NO), Kantonsspital Aarau (CH), University of Turin (IT), University of Edinburgh (GB)
National Science Foundation, Universität Basel, Singulex, European Commission, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Schweizerische Herzstiftung, Idorsia Pharmaceuticals
Reduced inequalities
Openalex Percentile: Top 12%
ECG Monitoring and Analysis
DOIOpen Access PDFOpenAlex
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