Taking a closer look at survival modeling with ECGs
AI-ECG: interpreting electrocardiograms for clinical decision-making
Electrocardiograms (ECGs) are a cheap and ubiquitous measure of the electrical activity of the heart. Advances in AI have demonstrated enormous prognostic value in these tests, above and beyond what clinicians and traditional computerized approaches have yielded. Our work develops and evaluates AI-ECG technology that turns routine electrocardiograms into clinically useful predictions. We study cardiac dysfunction, mortality, and longitudinal risk across pediatric and adult congenital heart disease, with an emphasis on robust, multicenter evaluation and practical clinical decision support.
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Selected Papers
Electrocardiogram-based deep learning to predict left ventricular systolic dysfunction in paediatric and adult congenital heart disease in the USA: a multicentre modelling study
The Lancet Digital Health
Electrocardiogram-based deep learning to predict mortality in paediatric and adult congenital heart disease
European Heart Journal
Deep survival analysis from adult and pediatric electrocardiograms: a multi-center benchmark study
BioData Mining
Deep Learning-Based Electrocardiogram Analysis Predicts Biventricular Dysfunction and Dilation in Congenital Heart Disease
Journal of the American College of Cardiology (JACC)
An ECG foundation model for generalizable cardiac function prediction across the lifespan
medRxiv