Efficacy of an artificial neural network algorithm based on thick-slab magnetic resonance cholangiopancreatography images for the automated diagnosis of common bile duct stones.

Journal: Journal of gastroenterology and hepatology
Published Date: Dec 1, 2021

Abstract

BACKGROUND AND AIM: Magnetic resonance cholangiopancreatography (MRCP) can accurately diagnose common bile duct (CBD) stones but is laborious to interpret. We developed an artificial neural network (ANN) capable of automatically assisting physicians with the diagnosis of CBD stones. This study aimed to evaluate the ANN's diagnostic performance for detecting CBD stones in thick-slab MRCP images and identify clinical factors predictive of accurate diagnosis.

Authors

  • Jong-Uk Hou
    School of Software, Hallym University, Chuncheon, Korea.
  • Se Woo Park
    Department of Internal Medicine, Hallym University College of Medicine, Chuncheon, Korea.
  • Seon Mee Park
    Department of Internal Medicine, Chungbuk National University College of Medicine, Cheongju, Korea.
  • Da Hae Park
    Division of Gastroenterology, Department of Internal Medicine, Hallym University Dongtan Sacred Heart Hospital, Hallym University College of Medicine, Gyeonggi-do, Korea.
  • Chan Hyuk Park
    Department of Internal Medicine, Hanyang University Guri Hospital, Hanyang University College of Medicine, Guri, Korea.
  • Seonjeong Min
    Department of Radiology, Hallym University Dongtan Sacred Heart Hospital, Hallym University College of Medicine, Gyeonggi-do, Korea.

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