Video-Based Deep Learning to Detect Dyssynergic Defecation with 3D High-Definition Anorectal Manometry.

Journal: Digestive diseases and sciences
PMID: 36401758

Abstract

BACKGROUND: We developed a deep learning algorithm to evaluate defecatory patterns to identify dyssynergic defecation using 3-dimensional high definition anal manometry (3D-HDAM).

Authors

  • Joshua J Levy
    DOE Joint Genome Institute, 2800 Mitchell Dr, Walnut Creek, CA, 94598, USA.
  • Christopher M Navas
    Section of Gastroenterology and Hepatology, Dartmouth-Hitchcock Health, One Medical Center Drive, Lebanon, NH, 03756, USA.
  • Joan A Chandra
    Section of Gastroenterology and Hepatology, Dartmouth-Hitchcock Health, One Medical Center Drive, Lebanon, NH, 03756, USA.
  • Brock C Christensen
    Department of Epidemiology, Lebanon, USA. Brock.C.Christensen@dartmouth.edu.
  • Louis J Vaickus
    Department of Pathology and Laboratory Medicine, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire.
  • Michael Curley
    Section of Gastroenterology and Hepatology, Dartmouth-Hitchcock Health, One Medical Center Drive, Lebanon, NH, 03756, USA.
  • William D Chey
    Division of Gastroenterology and Hepatology, Michigan Medicine, Ann Arbor, MI, USA.
  • Jason R Baker
    Division of Gastroenterology, Atrium Motility Laboratory, Atrium Health, Charlotte, NC, USA.
  • Eric D Shah
    Section of Gastroenterology and Hepatology, Dartmouth-Hitchcock Health, One Medical Center Drive, Lebanon, NH, 03756, USA. eric.d.shah@hitchcock.org.

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