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Background <p>Traditional surgical skill assessment tools are inherently subjective, prone to interobserver variability, and place a substantial burden on expert reviewers. This study aimed to evaluate the feasibility of a proof-of-concept artificial intelligence (AI)-based system for automated assessment of selected dimensions of technical performance in laparoscopic distal gastrectomy (LDG) using a phase recognition model and to construct a scoring system for risk-stratification in expert-based surgical performance assessment.</p> Methods <p>Surgical videos of LDG procedures performed between 2015 and 2021 were collected from multiple institutions across Japan. Each procedure was divided into nine phases. Based on Endoscopic Surgical Skill Qualification System (ESSQS) scores, surgical videos were classified into three proficiency groups (high, middle, and low). Parameters from the phase recognition model were integrated into a composite score, referred to as the Total AI score, which was compared across groups. A cutoff value was also established for risk-stratification based on ESSQS qualification status.</p> Results <p>A total of 1399 videos were collected, of which 256 were used to develop the phase recognition model, and 955 were used to construct and test the scoring system after excluding inappropriate cases. The phase recognition model achieved 90.3% accuracy. The Total AI score was significantly higher in the high proficiency group than in the middle (<i>p</i> = 0.003) and low proficiency (<i>p</i> = 0.010) groups. A cutoff of 67.3 identified a sensitivity of 95.7% and specificity of 25.0% for predicting ESSQS qualification status.</p> Conclusions <p>The Total AI score, derived from an automated phase recognition model, may serve as a proof-of-concept AI-based quantitative metric for the reproducible assessment of workflow efficiency and exposure quality in LDG and may help reduce expert reviewer workload through risk-stratification. </p> Graphical abstract <p></p>

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AI-based assessment of surgical efficiency and exposure quality in laparoscopic distal gastrectomy using a phase recognition model

  • 期刊论文

  • 发表日期：

    2026年7月27日

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  • Ayumi Umemiya,
  • Shin Takenaka,
  • Daichi Kitaguchi,
  • Shintaro Arakaki,
  • Masashi Wakabayashi,
  • Shuntaro Iuchi,
  • Nao Baba,
  • Risa Takai,
  • Yoshitsugu Yanagida,
  • Kimimasa Sasaki,
  • Norihito Kosugi,
  • Masahiro Yura,
  • Nobuyoshi Takeshita,
  • Takeo Fujita,
  • Takahiro Kinoshita,
  • Masaaki Ito

摘要

Background

Traditional surgical skill assessment tools are inherently subjective, prone to interobserver variability, and place a substantial burden on expert reviewers. This study aimed to evaluate the feasibility of a proof-of-concept artificial intelligence (AI)-based system for automated assessment of selected dimensions of technical performance in laparoscopic distal gastrectomy (LDG) using a phase recognition model and to construct a scoring system for risk-stratification in expert-based surgical performance assessment.

Methods

Surgical videos of LDG procedures performed between 2015 and 2021 were collected from multiple institutions across Japan. Each procedure was divided into nine phases. Based on Endoscopic Surgical Skill Qualification System (ESSQS) scores, surgical videos were classified into three proficiency groups (high, middle, and low). Parameters from the phase recognition model were integrated into a composite score, referred to as the Total AI score, which was compared across groups. A cutoff value was also established for risk-stratification based on ESSQS qualification status.

Results

A total of 1399 videos were collected, of which 256 were used to develop the phase recognition model, and 955 were used to construct and test the scoring system after excluding inappropriate cases. The phase recognition model achieved 90.3% accuracy. The Total AI score was significantly higher in the high proficiency group than in the middle (p = 0.003) and low proficiency (p = 0.010) groups. A cutoff of 67.3 identified a sensitivity of 95.7% and specificity of 25.0% for predicting ESSQS qualification status.

Conclusions

The Total AI score, derived from an automated phase recognition model, may serve as a proof-of-concept AI-based quantitative metric for the reproducible assessment of workflow efficiency and exposure quality in LDG and may help reduce expert reviewer workload through risk-stratification.

Graphical abstract