Electrocardiogram-based deep learning to predict mortality in paediatric and adult congenital heart disease.

Journal: European heart journal
PMID: 39387652

Abstract

BACKGROUND AND AIMS: Robust and convenient risk stratification of patients with paediatric and adult congenital heart disease (CHD) is lacking. This study aims to address this gap with an artificial intelligence-enhanced electrocardiogram (ECG) tool across the lifespan of a large, diverse cohort with CHD.

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.
  • 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.
  • William G La Cava
    Computational Health Informatics Program (W.G.L.C.), Boston Children's Hospital, MA.
  • Tal Geva
    Department of Cardiology, Boston Children's Hospital, Boston, MA, USA; Department of Pediatrics, Harvard Medical School, Boston, MA, USA.
  • Anne Marie Valente
    Department of Cardiology, Boston Children's Hospital, Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, 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.
  • Sunil J Ghelani
    Department of Cardiology, Boston Children's Hospital, Boston, Massachusetts, USA.

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