Detecting structural heart disease from electrocardiograms using AI.

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 echocardiography^1,2. Recent advances in machine learning applied to heart rhythm recordings have shown promise in identifying disease^3,4, 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 controlle

SUBMITTER: Poterucha TJ 

PROVIDER: S-EPMC12328201 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature