Deep learning-based artificial intelligence model for identifying swallow types in esophageal high-resolution manometry.

Journal: Neurogastroenterology and motility
PMID: 34709712

Abstract

BACKGROUND: This study aimed to build and evaluate a deep learning, artificial intelligence (AI) model to automatically classify swallow types based on raw data from esophageal high-resolution manometry (HRM).

Authors

  • Wenjun Kou
    Department of Medicine, Division of Gastroenterology and Hepatology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
  • Galal Osama Galal
    Anesthesiology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
  • Matthew William Klug
    Anesthesiology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
  • Vladislav Mukhin
    Anesthesiology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
  • Dustin A Carlson
    Department of Medicine, Division of Gastroenterology and Hepatology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
  • Mozziyar Etemadi
    From the School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta (O.T.I., M.B.P., A.Q.J., A.D., A.O.B.); Division of Cardiology (S.D., T.D.M., L.K.) and Department of Bioengineering and Therapeutic Sciences (S.R.), University of California, San Francisco; and Department of Anesthesiology and Department of Biomedical Engineering, Northwestern University, Chicago, IL (M.E., J.A.H.).
  • Peter J Kahrilas
    Department of Medicine, Division of Gastroenterology and Hepatology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
  • John E Pandolfino
    Department of Medicine, Division of Gastroenterology and Hepatology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.

Keywords

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