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

## TL;DR

This paper describes an AI system that improves safety and quality in liver surgery by accurately tracking instruments and identifying surgical phases in real time.

## Contribution

The paper introduces an upgraded AI Surgical Assistant with enhanced real-time recognition accuracy and reduced inter-operator variability for laparoscopic liver surgery.

## Key findings

- The upgraded ISA achieved 89% accuracy in real-time recognition of instruments and organs.
- Phase classification reached 91% accuracy, with critical phases showing high AUC values.
- Inter-operator variability was reduced to 14.3%, supporting standardized surgical alerts.

## Abstract

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.

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

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.

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.

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.