Deep-Learning Models for the Echocardiographic Assessment of Diastolic Dysfunction.

Journal: JACC. Cardiovascular imaging
Published Date: May 19, 2021

Abstract

OBJECTIVES: The authors explored a deep neural network (DeepNN) model that integrates multidimensional echocardiographic data to identify distinct patient subgroups with heart failure with preserved ejection fraction (HFpEF).

Authors

  • Ambarish Pandey
    Division of Cardiology, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA.
  • Nobuyuki Kagiyama
    West Virginia University Heart and Vascular Institute Morgantown WV.
  • Naveena Yanamala
    1 Exposure Assessment Branch, Health Effects Laboratory Division, National Institute for Occupational Safety and Health, Morgantown, West Virginia, USA.
  • Matthew W Segar
    Division of Cardiology, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA.
  • Jung S Cho
    Center for Clinical Innovation, Division of Cardiology, West Virginia University Heart and Vascular Institute, Morgantown, West Virginia, USA; Division of Cardiology, Daejeon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
  • Márton Tokodi
    Division of Cardiology, West Virginia University Heart & Vascular Institute, Morgantown, West Virginia.
  • Partho P Sengupta
    Division of Cardiovascular Diseases and Hypertension, Robert Wood Johnson University Hospital, and Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA.

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