Journal article
Using neural networks to autonomously assess adequacy in intraoperative cholangiograms
Henry Badgery, Yuning Zhou, James Bailey, Peter Brotchie, Lynn Chong, Daniel Croagh, Mark Page, Catherine E Davey, Matthew Read
Surgical Endoscopy | Springer | Published : 2024
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..
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Awarded by Epworth Foundation
Funding Acknowledgements
This research was undertaken using the LIEF HPC-GPGPU Facility hosted at the University of Melbourne. This Facility was established with the assistance of LIEF Grant LE170100200. We acknowledge the support of V7 labs for the use of the Darwin data labelling platform. This work is made possible with thanks to the generosity of donors of the Epworth Medical Foundation. Additional thanks to Cassius Fernando for data labelling assistance.