Automated identification of critical structures in laparoscopic cholecystectomy.

PURPOSE:Bile duct injury is a significant problem in laparoscopic cholecystectomy and can have grave consequences for patient outcomes. Automatic identification of the critical structures (cystic duct and cystic artery) could potentially reduce complications during surgery by helping the surgeon establish Critical View of Safety, or eventually may even provide real time intra-operative guidance. METHODS:A computer vision model was trained to identify the critical structures. Label relaxation enabled the model to cope with ambiguous spatial extent and high annotation variability. Pseudo-label self-supervision allowed the model to use unlabelled data, which can be particularly beneficial when scarce labelled data is available for training. Intrinsic variability in annotations was assessed across several annotators, quantifying the extent of annotation ambiguity and setting a baseline for model accuracy. RESULTS:Using 3050 labelled and 3682 unlabelled cholecystectomy frames, the model achieved an IoU of 65% and presence detection F1 score of 75%. Inter-annotator IoU agreement was 70%, demonstrating the model was near human-level agreement on average in this dataset. The model's outputs were validated by three expert surgeons, who confirmed that its outputs were accurate and promising for future usage. CONCLUSION:Identification of critical structures can achieve high accuracy, and is a promising step towards computer-assisted intervention in addition to potential applications in analytics and education. High accuracy and surgeon approval is maintained when detecting the structures separately as distinct classes. Future work will focus on guaranteeing safe identification of critical anatomy, including the bile duct, and validating the performance of automated approaches.