Machine Learning in Laparoscopic Surgery

Badgery, Henry; Zhou, Yuning; Siderellis, Athanasia; Read, Matthew; Davey, Catherine E.

doi:10.1007/978-981-19-1223-8_8

Cited by 4 publications

(1 citation statement)

References 30 publications

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“…Artificial intelligence (AI) is the use of computers or machines to to perform tasks that typically require human intelligence. Investment of AI in medical and surgical applications has surged in recent years, in part owing to access to improved computing power and data collection [ 9 ]. AI-based visual tasks and applications have been developed across a wide array of medical fields including radiology, surgery, endoscopy and pathology.…”

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confidence: 99%

Using neural networks to autonomously assess adequacy in intraoperative cholangiograms

Badgery,

Zhou,

Bailey

et al. 2024

Surg Endosc

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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. Methodology Semantic segmentation masks were applied to a dataset of 1000 cholangiograms with 10 classes. Classes corresponded to anatomy, filling defects and the cholangiogram catheter instrument. Segmentation masks were applied by a surgical trainee and reviewed by a radiologist. Two convolutional neural networks (CNNs), DeeplabV3+ and U-Net, were trained and validated using 900 (90%) labelled frames. Testing was conducted on 100 (10%) hold-out frames. CNN generated segmentation class masks were compared with ground truth segmentation masks to evaluate performance according to a pixel-wise comparison. Results The trained CNNs recognised all classes.. U-Net and DeeplabV3+ achieved a mean F1 of 0.64 and 0.70 respectively in class segmentation, excluding the background class. The presence of individual classes was correctly recognised in over 80% of cases. Given the limited local dataset, these results provide proof of concept in the development of an accurate and clinically useful tool to aid in the interpretation and quality control of intraoperative cholangiograms. Conclusion Our results demonstrate that a CNN can be trained to identify anatomical structures in IOC images. Future performance can be improved with the use of larger, more diverse training datasets. Implementation of this technology may provide cholangiogram quality control and improve intraoperative detection of ductal injuries or ductal injuries. Graphical abstract

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confidence: 99%