QTNet: Predicting Drug-Induced QT Prolongation With Artificial Intelligence-Enabled Electrocardiograms.

Journal: JACC. Clinical electrophysiology
PMID: 38703162

Abstract

BACKGROUND: Prediction of drug-induced long QT syndrome (diLQTS) is of critical importance given its association with torsades de pointes. There is no reliable method for the outpatient prediction of diLQTS.

Authors

  • Hao Zhang
    College of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou, 450002, China.
  • Constantine Tarabanis
    Leon H. Charney Division of Cardiology, Cardiac Electrophysiology, NYU Langone Health, New York University School of Medicine, New York, New York, USA.
  • Neil Jethani
    Department of Population Health, NYU Langone Health, New York University School of Medicine, New York, New York, USA; Courant Institute of Mathematical Sciences, New York University, New York, New York, USA.
  • Mark Goldstein
    Courant Institute of Mathematical Sciences, New York University, New York, New York, USA.
  • Silas Smith
    New York University School of Medicine, Ronald O. Perelman Department of Emergency Medicine, New York, NY, United States.
  • Larry Chinitz
    Leon H. Charney Division of Cardiology, NYU Langone Health, New York, NY, USA.
  • Rajesh Ranganath
    Department of Computer Science, New York University.
  • Yindalon Aphinyanaphongs
    Department of Population Health, New York University, New York.
  • Lior Jankelson
    Leon H. Charney Division of Cardiology, NYU Langone Health, New York, NY, USA.

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