Artificial Intelligence-Assisted Prediction of Bile Duct Bifurcation Site in Pure Laparoscopic Donor Right Hepatectomy: A Human-in-the-Loop Approach

Abstract

Accurate identification of the bile duct bifurcation site is crucial in pure laparoscopic donor right hepatectomy (PLDRH) for living donor liver transplantation. This study aimed to develop and evaluate an artificial intelligence (AI) model to predict the location of bile duct bifurcation during PLDRH procedures. In this single-institution retrospective feasibility study, we analyzed 55 PLDRH procedures performed between August 2021 and April 2022. We developed a deep learning model combining UNet with a MiT-B3 encoder, utilizing a human-in-the-loop approach. The model was trained on 150 manually annotated frames and refined using expert-reviewed pseudo-labels from an additional 901 frames. Model performance was evaluated using 5-fold cross-validation and an independent test set. The final model achieved 97% accuracy in clinical evaluation for 5-fold cross-validation and 93.3% accuracy on the independent test set. Quantitative metrics showed improvements from the initial to the final model, with mean Dice Similarity Coefficient increasing from 0.392 to 0.472 and Intersection over Union from 0.279 to 0.339. Sensitivity improved from 0.487 to 0.643, while specificity remained consistent at 0.993. This study demonstrates the potential of AI in accurately predicting the bile duct bifurcation site during PLDRH procedures. The human-in-the-loop approach proved effective in improving model performance and annotation efficiency. While challenges remain in bridging clinical accuracy and quantitative metrics, the high clinical accuracy suggests a promising step towards integrating AI into liver transplant surgery.