Pediatric ECG-Based Deep Learning to Predict Left Ventricular Dysfunction and Remodeling.

Journal: Circulation
PMID:

Abstract

BACKGROUND: Artificial intelligence-enhanced ECG analysis shows promise to detect ventricular dysfunction and remodeling in adult populations. However, its application to pediatric populations remains underexplored.

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.
  • William G La Cava
    Computational Health Informatics Program (W.G.L.C.), Boston Children's Hospital, MA.
  • Akhil Vaid
    Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA.
  • Girish N Nadkarni
    Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Sunil J Ghelani
    Department of Cardiology, Boston Children's Hospital, Boston, Massachusetts, USA.
  • Rebekah Mannix
    Department of Medicine, Division of Emergency Medicine (R.M.), 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.
  • Audrey Dionne
    Division of Pediatric Cardiology, CHU Ste-Justine, University of Montreal, 3175, Côte Sainte-Catherine, Montreal, QC, H3T 1C5, Canada.
  • Mark E Alexander
    Department of Cardiology (J.M., S.J.G., T.G., A.D., M.E.A., J.K.T.), Boston Children's Hospital, MA.
  • Son Q Duong
    The Charles Bronfman Institute of Personalized Medicine (A.V., G.N.N., S.Q.D.), Icahn School of Medicine at Mount Sinai, New York, NY.
  • 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.