Deep learning model to detect significant aortic regurgitation using electrocardiography.

Journal: Journal of cardiology
PMID: 34544652

Abstract

BACKGROUND: Aortic regurgitation (AR) is a common heart disease, with a relatively high prevalence of 4.9% in the Framingham Heart Study. Because the prevalence increases with advancing age, an upward shift in the age distribution may increase the burden of AR. To provide an effective screening method for AR, we developed a deep learning-based artificial intelligence algorithm for the diagnosis of significant AR using electrocardiography (ECG).

Authors

  • Shinnosuke Sawano
    Department of Cardiovascular Medicine, The University of Tokyo.
  • Satoshi Kodera
    Department of Cardiovascular Medicine, Graduate School of Medicine, The University of Tokyo.
  • Susumu Katsushika
    Department of Cardiovascular Medicine, The University of Tokyo.
  • Mitsuhiko Nakamoto
    Department of Cardiovascular Medicine, The University of Tokyo.
  • Kota Ninomiya
    Department of Cardiovascular Medicine, The University of Tokyo.
  • Hiroki Shinohara
    Department of Cardiovascular Medicine, Graduate School of Medicine, The University of Tokyo.
  • Yasutomi Higashikuni
    Department of Cardiovascular Medicine, Graduate School of Medicine, The University of Tokyo.
  • Koki Nakanishi
    Department of Cardiovascular Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
  • Tomoko Nakao
    Department of Cardiovascular Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
  • Tomohisa Seki
    Department of Cardiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582, Japan; Department of Emergency and Critical Care Medicine, Keio University School of Medicine, Tokyo 160-8582, Japan.
  • Norifumi Takeda
    Department of Cardiovascular Medicine, The University of Tokyo.
  • Katsuhito Fujiu
    Department of Cardiovascular Medicine, The University of Tokyo.
  • Masao Daimon
    Department of Cardiovascular Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
  • Hiroshi Akazawa
    Department of Cardiovascular Medicine, Graduate School of Medicine, The University of Tokyo.
  • Hiroyuki Morita
    Department of Cardiovascular Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
  • Issei Komuro
    Department of Cardiovascular Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.

Keywords

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