Automated recognition of critical anatomical structures in laparoscopic cholecystectomy using artificial intelligence
Jae Hyun Kwon, Jaewoong Kang, Soeui Kim, Jong Woo Lee, Jung-Woo Lee, Bum-Joo ChoIntroduction:
Despite the widespread use of minimally invasive surgery, bile duct injury (BDI) remains a significant complication of laparoscopic cholecystectomy (LC). Accurate identification of the common bile duct (CBD) and common hepatic duct (CHD) is critical for preventing BDI. This study aimed to develop and evaluate a deep learning model for segmenting the “CBD zone,” a semantically defined high-risk region within the hepatoduodenal ligament encompassing the CHD, CBD, and adjacent arterial structures, to assist with anatomical recognition during LC.
Methods:
Surgical videos from 100 patients who underwent elective LC, with a total recorded duration of less than 20 minutes, were retrospectively analyzed. Three board-certified hepatobiliary surgeons annotated the CBD zone and gallbladder. A total of 1045 frames were extracted and divided into training, tuning, and internal test sets. Each case was graded according to the Parkland Grading System to stratify cholecystitis severity. Three convolutional neural networks – U-Net, LRASPP-MobileNetV3, and DeepLabv3-ResNet101 – were trained. Segmentation performance was evaluated using the Dice score and Intersection over Union (IoU). Simulation-based expert assessment was performed on internal and external datasets using pass/fail ratings.
Results:
DeepLabv3 demonstrated the best segmentation performance, with Dice scores of 0.910 for the CBD zone and 0.903 for the gallbladder. The mean IoU was 0.857 for the CBD zone and 0.847 for the gallbladder. In simulation-based expert assessment, the mean pass rate across three internal experiments was 96.0%. In the external dataset, the mean pass rate was 75.8%.
Conclusion:
We developed an AI-based segmentation model for identifying the CBD zone and gallbladder during LC. The model demonstrated strong segmentation performance and preliminary feasibility in external validation. Further prospective and multicenter studies are required to determine its practical utility in intraoperative settings.