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Foundation Models For Electrocardiogram Interpretation: Clinical Implications.

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Foundation Models For Electrocardiogram Interpretation: Clinical Implications.

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Foundation models for electrocardiogram interpretation: clinical implications.

Alexis Nolin-Lapalme^1,2,3,4, Achille Sowa^1,2,4, Jacques Delfrate^2,4

¹Department of Biochemistry and Molecular Medicine, Faculty of Medicine, University of Montreal, Montreal, Quebec, Canada H3C 3J7.

European Heart Journal

|January 22, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces two open-source artificial intelligence (AI) models for electrocardiogram (ECG) interpretation. Self-supervised learning (SSL) shows promise for improving AI diagnostics in cardiac care, especially with limited data.

Keywords:

Artificial intelligence CLSA Electrocardiogram Fairness Foundation model Generalizability Privacy UK Biobank

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Area of Science:

Artificial Intelligence in Medicine
Cardiovascular Diagnostics
Machine Learning for Healthcare

Background:

Existing AI for ECG interpretation often lacks generalizability and relies on supervised learning (SL) with extensive labeled data.
Self-supervised learning (SSL) offers a potential solution by learning from unlabeled data, overcoming limitations of traditional methods.
This study addresses the need for adaptable, open-source AI solutions in cardiac diagnostics.

Purpose of the Study:

To develop and compare two open-source foundational ECG models: DeepECG-SL (supervised) and DeepECG-SSL (self-supervised).
To evaluate the generalizability, fairness, and performance of SSL versus SL in ECG interpretation across diverse clinical settings.
To provide accessible AI tools for robust and data-efficient cardiac diagnostics.

Main Methods:

Trained two models, DeepECG-SL and DeepECG-SSL, on over 1 million ECGs to predict 77 cardiac conditions.
DeepECG-SSL utilized contrastive learning and masked lead modeling on unlabeled data before fine-tuning.
Performance was assessed across seven private and four public multilingual healthcare systems, including fairness and privacy evaluations.

Main Results:

Both models achieved high performance (AUROCs ~0.98-0.99) across internal, public, and private external datasets.
DeepECG-SSL showed superior performance in limited-data tasks like long QT syndrome genotype classification and atrial fibrillation risk prediction.
Fairness analyses indicated minimal disparities across age and sex groups for both models.

Conclusions:

Self-supervised learning (SSL) is a promising paradigm for ECG analysis, enhancing accessibility, generalizability, and fairness in AI-driven cardiac diagnostics.
The open-source release of models, tools, and code aims to foster robust, data-efficient AI diagnostics.
SSL models are particularly beneficial in clinical settings with limited annotated data.