Identification of patients with malignant biliary strictures using a cholangioscopy-based deep learning artificial intelligence (with video).

Journal: Gastrointestinal endoscopy
PMID: 36007584

Abstract

BACKGROUND AND AIMS: Accurately diagnosing malignant biliary strictures (MBSs) as benign or malignant remains challenging. It has been suggested that direct visualization and interpretation of cholangioscopy images provide greater accuracy for stricture classification than current sampling techniques (ie, brush cytology and forceps biopsy sampling) using ERCP. We aimed to develop a convolutional neural network (CNN) model capable of accurate stricture classification and real-time evaluation based solely on cholangioscopy image analysis.

Authors

  • Neil B Marya
    Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota.
  • Patrick D Powers
    Independent Researcher, Chelsea, Massachusetts.
  • Bret T Petersen
    Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA.
  • Ryan Law
    Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA.
  • Andrew Storm
    Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA.
  • Rami R Abusaleh
    Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA.
  • Prashanth Rau
    Division of Gastroenterology, UMass Chan Medical School, Worcester, Massachusetts, USA.
  • Courtney Stead
    Department of Medicine, UMass Chan Medical School, Worcester, Massachusetts, USA.
  • Michael J Levy
    Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota.
  • John Martin
    Butterfly Network, Inc., Guilford, CT 06437.
  • Eric J Vargas
    Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA.
  • Barham K Abu Dayyeh
    Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota.
  • Vinay Chandrasekhara
    Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota.

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