Using neural networks to autonomously assess adequacy in intraoperative cholangiograms.

Journal: Surgical endoscopy
PMID: 38561583

Abstract

BACKGROUND: Intraoperative cholangiography (IOC) is a contrast-enhanced X-ray acquired during laparoscopic cholecystectomy. IOC images the biliary tree whereby filling defects, anatomical anomalies and duct injuries can be identified. In Australia, IOC are performed in over 81% of cholecystectomies compared with 20 to 30% internationally (Welfare AIoHa in Australian Atlas of Healthcare Variation, 2017). In this study, we aim to train artificial intelligence (AI) algorithms to interpret anatomy and recognise abnormalities in IOC images. This has potential utility in (a) intraoperative safety mechanisms to limit the risk of missed ductal injury or stone, (b) surgical training and coaching, and (c) auditing of cholangiogram quality.

Authors

  • Henry Badgery
    Department of Upper Gastrointestinal Surgery, St Vincent's Hospital Melbourne, Melbourne, Australia. henry.badgery@svha.org.au.
  • Yuning Zhou
    Department of Biomedical Engineering, The University of Melbourne, Parkville, Australia.
  • James Bailey
  • Peter Brotchie
    St Vincent's Hospital, Melbourne, Victoria, Australia.
  • Lynn Chong
    Department of Upper Gastrointestinal Surgery, St Vincent's Hospital Melbourne, Melbourne, Australia.
  • Daniel Croagh
    Department of Upper Gastrointestinal Surgery, St Vincent's Hospital Melbourne, Melbourne, Australia.
  • Mark Page
    Department of Radiology, St Vincent's Hospital Melbourne, Melbourne, Australia.
  • Catherine E Davey
    Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia. catherine.davey@unimelb.edu.au.
  • Matthew Read
    Department of Upper Gastrointestinal Surgery, St Vincent's Hospital Melbourne, Melbourne, Australia.

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