Detecting structural heart disease from electrocardiograms using AI

• 成果类型: Article
• 署名作者: Poterucha, Timothy J.; Jing, Linyuan; Ricart, Ramon Pimentel; Adjei-Mosi, Michael; Finer, Joshua; Hartzel, Dustin; Kelsey, Christopher; Long, Aaron; Rocha, Daniel; Ruhl, Jeffrey A.; vanMaanen, David; Probst, Marc A.; Daniels, Brock; Joshi, Shalmali D.; Tastet, Olivier; Corbin, Denis; Avram, Robert; Barrios, Joshua P.; Tison, Geoffrey H.; Chiu, I-Min; Ouyang, David; Volodarskiy, Alexander; Castillo, Michelle; Oliver, Francisco A. Roedan; Malta, Paloma P.; Ye, Siqin; Rosner, Gregg F.; Dizon, Jose M.; Ali, Shah R.; Liu, Qi; Bradley, Corey K.; Vaishnava, Prashant; Waksmonski, Carol A.; DeFilippis, Ersilia M.; Agarwal, Vratika; Lebehn, Mark; Kampaktsis, Polydoros N.; Shames, Sofia; Beecy, Ashley N.; Kumaraiah, Deepa; Homma, Shunichi; Schwartz, Allan; Hahn, Rebecca T.; Leon, Martin; Einstein, Andrew J.; Maurer, Mathew S.; Hartman, Heidi S.; Hughes, John Weston; Haggerty, Christopher M.; Elias, Pierre
• 署名单位: Columbia University; NewYork-Presbyterian Hospital; NewYork-Presbyterian Hospital; Columbia University; Columbia University; Columbia University; NewYork-Presbyterian Hospital; Cornell University; Weill Cornell Medicine; Cornell University; Weill Cornell Medicine; Universite de Montreal; University of California System; University of California San Francisco; University of California System; University of California San Francisco; Cedars Sinai Medical Center; Chang Gung Memorial Hospital; Cornell University; Weill Cornell Medicine; Cardiovascular Research Foundation (CRF); Columbia University; NewYork-Presbyterian Hospital
• 刊物名称: Nature
• ISSN/ISSBN: 0028-1578
• DOI: 10.1038/s41586-025-09227-0
• 发表日期: 2025-08-07
• 关键词: aortic-valve stenosis; american society; european association; recommendations; echocardiography; regurgitation; update

摘要

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.