Prediction of adverse cardiovascular events in children using artificial intelligence-based electrocardiogram.

Journal: International journal of cardiology
PMID: 38579941

Abstract

BACKGROUND: Convolutional neural networks (CNNs) have emerged as a novel method for evaluating heart failure (HF) in adult electrocardiograms (ECGs). However, such CNNs are not applicable to pediatric HF, where abnormal anatomy of congenital heart defects plays an important role. ECG-based CNNs reflecting neurohormonal activation (NHA) may be a useful marker of pediatric HF. This study aimed to develop and validate an ECG-derived marker of pediatric HF that reflects the risk of future cardiovascular events.

Authors

  • Yoshitsugu Nogimori
    Department of Pediatrics, The University of Tokyo Hospital, Japan.
  • Kaname Sato
    Department of Pediatrics, The University of Tokyo Hospital, Japan.
  • Koichi Takamizawa
    Department of Pediatrics, The University of Tokyo Hospital, Japan.
  • Yosuke Ogawa
    Department of Pediatrics, The University of Tokyo Hospital, Japan.
  • Yu Tanaka
    Department of Pediatrics, The University of Tokyo Hospital, Japan.
  • Kazuhiro Shiraga
    Department of Pediatrics, The University of Tokyo Hospital, Japan.
  • Hitomi Masuda
    Department of Pediatrics, The University of Tokyo Hospital, Japan.
  • Hikoro Matsui
    Department of Pediatrics, The University of Tokyo Hospital, Japan.
  • Motohiro Kato
    Department of Pediatrics, The University of Tokyo Hospital, Japan.
  • Masao Daimon
    Department of Cardiovascular Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
  • Katsuhito Fujiu
    Department of Cardiovascular Medicine, The University of Tokyo.
  • Ryo Inuzuka
    Department of Pediatrics, The University of Tokyo Hospital, Japan. Electronic address: inuzukar-tky@g.ecc.u-tokyo.ac.jp.

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