Development of an AI-driven digital assistance system for real-time safety evaluation and quality control in laparoscopic liver surgery.

Background: By performing AI-driven workflow analysis, intelligent surgical systems can provide real-time intraoperative quality control and alerts. We have upgraded an Intelligent Surgical Assistant (ISA) through integrating a redesigned hierarchical recognition algorithm, an expanded surgical dataset, and an optimized real-time intraoperative feedback framework.

Objective: We aimed to assess the accuracy of the ISA in real-time instrument tracking, organ segmentation, and phase classification during laparoscopic hemi-hepatectomy.

Methods: In this retrospective multi-center analysis, a total of 142861 annotated frames were collected from 403 laparoscopic hemi-hepatectomy videos across 4 centers to build a comprehensive database of surgical video annotations. Each frame was labeled for surgical phase, organs, and instruments. The algorithm in the ISA was retrained using a hybrid deep learning framework integrating instrument tracking, organ segmentation, and phase classification. We then established a scoring system for surgical image recognition and evaluated the algorithm's recognition accuracy and inter-operator consistency across different surgical teams.

Results: The upgraded ISA achieved an accuracy of 89% in real-time recognition of instruments and organs. The programmatic phase classification for laparoscopic hemi-hepatectomy reached an average accuracy of 91% (p<0.001), enabling a correct recognition of surgical events. The inter-operator variability in recognition was reduced to 14.3%, highlighting the potential of AI-assisted quality control to standardize intraoperative alerts. Overall, the ISA demonstrated high precision and consistency in phase recognition and operative field evaluation across all phases (accuracy >87%, specificity ~90% in each phase). Notably, critical phases (Phase 1 and Phase 5) were identified with an exceptional accuracy area under the curve (AUC 0.96 in Phase 1; AUC 0.87 in Phase 5), indicating that key surgical procedures could be phased with very low false-alarm rates.

Conclusions: The optimized ISA provides a highly accurate real-time interpretation of surgical phases and a strong potential to standardize surgical procedures, thus guaranteeing the outcomes and safety of laparoscopic hemi-hepatectomy.