AUTHOR=Peng Zi-Yang , Wang Zhi-Bo , Yan Yan , Peng Hao-Qian , Ma Yong-Tai , Li Yu-Tong , Ren Yao-Xing , Xiang Jun-Xi , Guo Kun , Wang Gang , Duan Jian-Feng , Li Xiao-Wen , Guan Yu , Liu Xue-Min , Wu Rong-Qian , Lyu Yi , Yu Li 

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

JOURNAL=Frontiers in Oncology

VOLUME=Volume 15 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1678525

DOI=10.3389/fonc.2025.1678525

ISSN=2234-943X

ABSTRACT=BackgroundBy 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.ObjectiveWe aimed to assess the accuracy of the ISA in real-time instrument tracking, organ segmentation, and phase classification during laparoscopic hemi-hepatectomy.MethodsIn 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.ResultsThe 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.ConclusionsThe 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.