Background: Pure laparoscopic donor hepatectomy (PLDH) has become a standard procurement practice for living donor liver transplantation in expert centers. During the procedures of PLDH, a good anatomical approach for donor bile duct division is crucial to avoid multiple bile duct openings, which increases the risk of biliary complications for the recipient. This study was designed to develop a deep learning-based artificial intelligence model to identify biliary structures intraoperatively, helping to determine the optimal transaction site. Methods: Semantic segmentation of the bile duct was performed using a convolutional neural network-based approach. Deep-LabV3+ was utilized as the model with the ResNet as a backbone. Ground truth annotations were generated with the help of images of the bile duct under infrared fluoroscopy with indocyanine green by a single surgeon. The dice coefficient was utilized as an evaluation metric for the proposed model. Results: Three hundred images of the biliary structure were extracted from 30 PLDH videos, 80% of images were used as train dataset, and 20% were used for validation dataset. As a result, the model predicted the area of the bile duct with a precision of 0.66. Conclusions: Intraoperative artificial intelligence-guided bile duct division can be used for PLDH. This technology may provide real-time guidance and improve surgical outcomes.
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