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Background <p>In laparoscopic distal gastrectomy (LDG), intraoperative bleeding directly reflects surgical proficiency, including tissue handling and hemostasis. Conventional assessments of surgical skills, such as global operative assessment of laparoscopic skills, are subjective and time-consuming. Bleeding events can serve as an objective indicator for automated skill evaluation in this procedure. This study aimed to assess the feasibility of using an automated machine learning approach to objectively evaluate surgical skills during LDG by quantifying blood pixels in intraoperative videos.</p> Methods <p>A logistic regression-based machine learning model was developed to classify pixels as blood or non-blood based on RGB values. A total of 1008 images extracted from 28 LDG videos, comprising 234,699 blood pixels and 376,579 non-blood pixels, were used for training, validation, and testing. Additionally, LDG videos submitted for the endoscopic surgery skills qualification system between 2016 and 2021 were analyzed. Blood pixel counts per frame were calculated for both overall and specific lymphadenectomy phases and were then compared among the high-score (<i>n</i> = 42), low-score (<i>n</i> = 24), and novice groups (<i>n</i> = 22).</p> Results <p>The model achieved an overall accuracy of 94.0%, with a sensitivity of 99.8% and specificity of 90.4%. Analysis of the endoscopic surgery skills qualification system videos revealed significantly lower blood pixel counts per frame in the high-score group compared with the novice group (high-score group 561.8 [279.8–937.5] vs. novice group 1662.9 [660–3076], <i>p</i> = 0.003), particularly during lymphadenectomy on the right greater curvature (high-score group 761 [244.9–1058.3] vs. novice group 2140.1 [650.4–2986.4], <i>p</i> = 0.006) and suprapancreatic lymphadenectomy (high-score group 634.4 [205–948.2] vs. novice group 1867.4 [1011.9–3925.3], <i>p</i> &lt; 0.001).</p> Conclusions <p>This study demonstrates the feasibility of an automated machine learning-based approach to quantify blood pixels during LDG as an objective indicator of surgical skill. This method may provide a foundation for a more objective and efficient assessment of surgical performance.</p> Graphical abstract <p></p>

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Machine learning-driven automated evaluation of surgical skills during laparoscopic distal gastrectomy based on blood pixel analysis

  • 期刊论文

  • 发表日期：

    2026年6月15日

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  • Shintaro Arakaki,
  • Shin Takenaka,
  • Daichi Kitaguchi,
  • Tateo Ogane,
  • Kimimasa Sasaki,
  • Norihito Kosugi,
  • Xue Zhao,
  • Shuntaro Iuchi,
  • Nao Baba,
  • Yoshitsugu Yanagida,
  • Risa Takai,
  • Nobuyoshi Takeshita,
  • Mitsuhisa Takatsuki,
  • Masaaki Ito

摘要

Background

In laparoscopic distal gastrectomy (LDG), intraoperative bleeding directly reflects surgical proficiency, including tissue handling and hemostasis. Conventional assessments of surgical skills, such as global operative assessment of laparoscopic skills, are subjective and time-consuming. Bleeding events can serve as an objective indicator for automated skill evaluation in this procedure. This study aimed to assess the feasibility of using an automated machine learning approach to objectively evaluate surgical skills during LDG by quantifying blood pixels in intraoperative videos.

Methods

A logistic regression-based machine learning model was developed to classify pixels as blood or non-blood based on RGB values. A total of 1008 images extracted from 28 LDG videos, comprising 234,699 blood pixels and 376,579 non-blood pixels, were used for training, validation, and testing. Additionally, LDG videos submitted for the endoscopic surgery skills qualification system between 2016 and 2021 were analyzed. Blood pixel counts per frame were calculated for both overall and specific lymphadenectomy phases and were then compared among the high-score (n = 42), low-score (n = 24), and novice groups (n = 22).

Results

The model achieved an overall accuracy of 94.0%, with a sensitivity of 99.8% and specificity of 90.4%. Analysis of the endoscopic surgery skills qualification system videos revealed significantly lower blood pixel counts per frame in the high-score group compared with the novice group (high-score group 561.8 [279.8–937.5] vs. novice group 1662.9 [660–3076], p = 0.003), particularly during lymphadenectomy on the right greater curvature (high-score group 761 [244.9–1058.3] vs. novice group 2140.1 [650.4–2986.4], p = 0.006) and suprapancreatic lymphadenectomy (high-score group 634.4 [205–948.2] vs. novice group 1867.4 [1011.9–3925.3], p < 0.001).

Conclusions

This study demonstrates the feasibility of an automated machine learning-based approach to quantify blood pixels during LDG as an objective indicator of surgical skill. This method may provide a foundation for a more objective and efficient assessment of surgical performance.

Graphical abstract