Using artificial intelligence to predict choledocholithiasis: can machine learning models abate the use of MRCP in patients with biliary dysfunction?

Journal: ANZ journal of surgery
Published Date:

Abstract

BACKGROUND: Prompt diagnosis of choledocholithiasis is crucial for reducing disease severity, preventing complications and minimizing length of stay. Magnetic resonance cholangiopancreatography (MRCP) is commonly used to evaluate patients with suspected choledocholithiasis but is expensive and may delay definitive intervention. To optimize patient care and resource utilization, we have developed five machine learning models that predict a patients' risk of choledocholithiasis based on clinical presentation and pre-MRCP investigation results.

Authors

  • Joshua Blum
    Department of General Surgery, Royal Hobart Hospital, Hobart, Tasmania, Australia.
  • Sam Hunn
    Department of General Surgery, Royal Hobart Hospital, Hobart, Tasmania, Australia.
  • Jules Smith
    Department of General Surgery, Royal Hobart Hospital, Hobart, Tasmania, Australia.
  • Fa Yu Chan
    Tasmanian School of Medicine, University of Tasmania, Hobart, Tasmania, Australia.
  • Richard Turner
    Department of General Surgery, Royal Hobart Hospital, Hobart, Tasmania, Australia.