Electrocardiogram-based deep learning to predict left ventricular systolic dysfunction in paediatric and adult congenital heart disease in the USA: a multicentre modelling study.

Journal: The Lancet. Digital health
PMID: 40148010

Abstract

BACKGROUND: Left ventricular systolic dysfunction (LVSD) is independently associated with cardiovascular events in patients with congenital heart disease. Although artificial intelligence-enhanced electrocardiogram (AI-ECG) analysis is predictive of LVSD in the general adult population, it has yet to be applied comprehensively across congenital heart disease lesions.

Authors

  • Joshua Mayourian
    Department of Cardiology (J.M., S.J.G., T.G., A.D., M.E.A., J.K.T.), Boston Children's Hospital, MA.
  • Ivor B Asztalos
    Division of Pediatric Cardiology, Perelman School of Medicine at the University of Pennsylvania, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
  • Amr El-Bokl
    Department of Cardiology, Boston Children's Hospital, Boston, MA, USA.
  • Platon Lukyanenko
    Department of Pediatrics, Computational Health Informatics Program, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
  • Ryan L Kobayashi
    Department of Cardiology, Boston Children's Hospital, Department of Pediatrics, Harvard Medical School, Boston, MA, USA.
  • William G La Cava
    Computational Health Informatics Program (W.G.L.C.), Boston Children's Hospital, MA.
  • Sunil J Ghelani
    Department of Cardiology, Boston Children's Hospital, Boston, Massachusetts, USA.
  • Victoria L Vetter
    Division of Pediatric Cardiology, Perelman School of Medicine at the University of Pennsylvania, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
  • John K Triedman
    Department of Cardiology (J.M., S.J.G., T.G., A.D., M.E.A., J.K.T.), Boston Children's Hospital, MA.

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