Detecting structural heart disease from electrocardiograms using AI
Timothy J. Poterucha, Linyuan Jing, Richard Ricart, Michael Adjei-Mosi, Joshua Finer, Dustin N. Hartzel, C. D. Kelsey, Aaron S. Long, D. Rocha, Jeffrey Ruhl, David P. vanMaanen, Marc A. Probst, Brock Daniels, Shalmali Joshi, Olivier Tastet, Denis Corbin, Robert Avram, Joshua Barrios, Geoffrey H. Tison, I-Min Chiu, David Ouyang, Alexander Volodarskiy, Michelle Castillo, FRANCIS H. OLIVER, Paloma P Malta, Siqin Ye, Gregg Rosner, José Dizon, Shah Raj Ali, Qi Liu, Corey Bradley, Prashant Vaishnava, Carol A. Waksmonski, Ersilia M. DeFilippis, Vratika Agarwal, Mark Lebehn, Polydoros Ν. Kampaktsis, Sofia Shames, Ashley Beecy, Deepa Kumaraiah, Shunichi Homma, Allan Schwartz, Rebecca T. Hahn, Martin B. Leon, Andrew J. Einstein, Mathew S. Maurer, Heidi Hartman, J. Weston Hughes, Christopher M. Haggerty, Pierre Elias
Abstract
Early detection of structural heart disease is critical to improving outcomes, but widespread screening remains limited by the cost and accessibility of imaging tools such as echocardiography1,2. Recent advances in machine learning applied to heart rhythm recordings have shown promise in identifying disease3,4, although previous work has been limited by development in narrow populations or targeting only select heart conditions5. Here we introduce a deep learning model, EchoNext, trained on more than 1 million heart rhythm and imaging records across a large and diverse health system to detect many forms of structural heart disease. The model demonstrated high diagnostic accuracy in internal and external validation, outperforming cardiologists in a controlled evaluation and showing consistent performance across different care settings and racial and/or ethnic groups. The models were prospectively evaluated in a clinical trial of patients without previous cardiac imaging, successfully identifying previously undiagnosed heart disease. These findings support the potential of artificial intelligence to expand access to heart disease screening at scale. To enable further development and transparency, we have publicly released model weights and a large, annotated dataset linking heart rhythm data to imaging-based diagnoses. EchoNext, a deep learning model for electrocardiograms trained and validated in diverse health systems, successfully detects many forms of structural heart disease, supporting the potential of artificial intelligence to expand access to heart disease screening at scale.