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

作者：Zi‐Yang Peng, Zhibo Wang, Yan Yan, Hao-Qian Peng, Yifan Ma, Yutong Li, Yaoxing Ren, Junxi Xiang, Kun Guo, Gang Wang, Jin-Rui Duan, Xiaowen Li, Yu Guan, Xuemin Liu, Rongqian Wu, Yi Lyu, Yu Li · 发表于：Frontiers in Oncology · 年份：2025 · DOI：10.3389/fonc.2025.1678525 · 被引用次数：3 · 研究领域：Surgical Simulation and Training、Artificial Intelligence in Healthcare and Education、Advanced Radiotherapy Techniques

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 w...