Detecting structural heart disease from electrocardiograms using AI

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…

• AH
  American Heart Association

  Award: 58275/AHA
• PO
  Patient-Centered Outcomes Research Institute
• AF
  Amyloidosis Foundation
• FI
  Fondation Institut de Cardiologie de Montréal
• ID
  Institut de Cardiologie de Montréal
• NI
  National Institutes of Health

  Award: R01 grant
• NH
  National Heart, Lung, and Blood Institute

  Award: R01HL149680