Development and external validation of a contrastive learning foundation model for ECG-based prediction of cardiovascular diseases and outcomes.

Michael Ko¹, Matteo Gadaleta², Eric J Topol², Evan D Muse³, Giorgio Quer⁴

¹Scripps Research Translational Institute, La Jolla, CA, USA; Halıcıoğlu Data Science Institute, UC San Diego, La Jolla, CA, USA.

²Scripps Research Translational Institute, La Jolla, CA, USA.

³Scripps Research Translational Institute, La Jolla, CA, USA; Scripps Clinic, La Jolla, CA, USA.

⁴Scripps Research Translational Institute, La Jolla, CA, USA. Electronic address: gquer@scripps.edu.

PubMed DOI

Abstract

BACKGROUND: Automated electrocardiogram (ECG) assessment tools to assist clinicians in diagnosis have improved substantially over the past decade; however, the full diagnostic and predictive potential of ECGs is limited when using traditional machine learning approaches because of an over-reliance on task-specific labels. We aimed to develop a large-scale ECG foundation model, ECG-CLIP, with impro…

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