Related Experiment Video
Updated: Oct 28, 2026
Three-dimensional Location Approach with Silk Thread Guided Laparoscopic Segmentectomy for Liver Tumor
Published on: May 23, 2025
Proof-of-concept study toward the automated evaluation of surgical skills in laparoscopic liver resection using
Keishiro Aoki1,2,3, Shin Kobayashi1, Kimimasa Sasaki1,2
1Department of Hepatobiliary and Pancreatic Surgery, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
Background:
Maintaining a dry surgical field is fundamental to safe laparoscopic hepatectomy (LH); however, formal certification systems rely on labor-intensive expert review of unedited surgical videos. This study evaluated the association between AI-derived blood pixel count (BPC), which quantifies redness of the hepatic transection plane, and Endoscopic Surgical Skill Qualification System (ESSQS)-based surgical proficiency, and assessed its discriminatory performance for Low-score and Failed videos.
Methods:
We retrospectively analyzed 146 LH videos submitted to the ESSQS of the Japan Society for Endoscopic Surgery in 2020-2021. Bleeding regions were segmented using DeepLabv3+. BPC was calculated as the total number of pixels classified as bleeding across all sampled frames. Outcomes were Pass/Fail classification using the 70-point cutoff and the Low-score group, defined as ESSQS scores ≤ mean - 1 standard deviation. ROC analysis assessed discrimination. A BPC threshold with specificity ≥90% was selected.
Results:
The median BPC was 1,741,749 pixels (range, 7,944-10,507,218). BPC increased monotonically from the High-score group to the Low-score group (P < 0.001), and the Failed group had higher BPC than the Passed group (P = 0.001). The AUC of BPC was 0.708 (95% CI, 0.599-0.817) for identifying the Low-score group and 0.648 (95% CI, 0.546-0.751) for identifying the Failed group. Using a BPC threshold of 4,794,565 pixels, specificity and sensitivity for Low-score identification were 90.2 and 29.2%, respectively. In low-difficulty cases, the AUC for identifying the Low-score group was 0.784. When the same threshold was applied to Pass/Fail classification, no Passed video had a BPC at or above the threshold.
Conclusions:
AI-based BPC was associated with ESSQS-based surgical proficiency in LH. This objective, scalable, high-specificity metric may help flag procedures warranting expert review and timely feedback, particularly in low-difficulty cases. External validation and prospective implementation studies are warranted.