Artificial Intelligence–Based Surgical Phase Analysis Enables Objective Assessment of Surgeon Skill in Robotic Distal Gastrectomy: A Multicenter Study
- 1Gastric Surgery Division National Cancer Center Hospital East Chiba Japan
- 2Department for the Promotion of Medical Device Innovation National Cancer Center Hospital East Chiba Japan
Abstract
Aim: Artificial intelligence (AI)–based surgical video analysis can automate time‐consuming manual assessments and enable objective characterization of surgical workflows. We aimed to construct a large, multicenter, fully annotated dataset of robotic distal gastrectomy (RDG) videos and evaluate the feasibility and performance of an AI model for surgical phase recognition. We further explored whether AI‐derived phase‐specific metrics could characterize phase‐level performance differences according to surgeon experience.
Methods: We developed an image classification model to automatically identify surgical phases in RDG videos. Experienced gastric surgeons annotated nine predefined surgical phases on a frame‐by‐frame basis (1 fps). Model performance was assessed using accuracy, precision, recall, and F1‐score. In an exploratory analysis, the duration of each AI‐predicted surgical phase was quantified and compared according to each surgeon's robotic case volume.
Results: We analyzed 137 RDG videos collected from 15 institutions. The nine‐phase recognition model achieved an overall accuracy of 87.0%. Among the surgical phases, the AI‐predicted duration of right‐sided greater curvature lymphadenectomy was shorter for surgeons with higher robotic case volume than those with lower volume (3789 vs. 2547 frames, p < 0.01), indicating phase‐specific differences in operative performance.
Conclusions: The proposed AI model demonstrated robust performance in multicenter surgical phase recognition. AI‐based surgical phase analysis enabled objective characterization of phase‐specific operative profiles according to surgeon experience. These findings support the feasibility of surgical phase analysis as an exploratory framework for performance profiling and educational support in robotic gastrectomy. However, generalizability may be limited owing to institutional overlap between the training and validation datasets.
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BibTeX
@article{komatsu2026artificial,
title = {{Artificial Intelligence–Based Surgical Phase Analysis Enables Objective Assessment of Surgeon Skill in Robotic Distal Gastrectomy: A Multicenter Study}},
author = {Masaru Komatsu and Daichi Kitaguchi and Yoichi Ninomiya and Yuya Takabe and Mitsumasa Yoshida and Masahiro Yura and Nobuyoshi Takeshita and Takahiro Kinoshita and Masaaki Ito},
journal = {Annals of Gastroenterological Surgery},
year = {2026},
month = jul,
doi = {10.1002/ags3.70249},
url = {https://doi.org/10.1002/ags3.70249},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Artificial Intelligence–Based Surgical Phase Analysis Enables Objective Assessment of Surgeon Skill in Robotic Distal Gastrectomy: A Multicenter Study
AU - Masaru Komatsu
AU - Daichi Kitaguchi
AU - Yoichi Ninomiya
AU - Yuya Takabe
AU - Mitsumasa Yoshida
AU - Masahiro Yura
AU - Nobuyoshi Takeshita
AU - Takahiro Kinoshita
AU - Masaaki Ito
PY - 2026
DA - 2026/07/15
JO - Annals of Gastroenterological Surgery
DO - 10.1002/ags3.70249
UR - https://doi.org/10.1002/ags3.70249
AB - Aim: Artificial intelligence (AI)–based surgical video analysis can automate time‐consuming manual assessments and enable objective characterization of surgical workflows. We aimed to construct a large, multicenter, fully annotated dataset of robotic distal gastrectomy (RDG) videos and evaluate the feasibility and performance of an AI model for surgical phase recognition. We further explored whether AI‐derived phase‐specific metrics could characterize phase‐level performance differences according to surgeon experience. Methods: We developed an image classification model to automatically identify surgical phases in RDG videos. Experienced gastric surgeons annotated nine predefined surgical phases on a frame‐by‐frame basis (1 fps). Model performance was assessed using accuracy, precision, recall, and F1‐score. In an exploratory analysis, the duration of each AI‐predicted surgical phase was quantified and compared according to each surgeon's robotic case volume. Results: We analyzed 137 RDG videos collected from 15 institutions. The nine‐phase recognition model achieved an overall accuracy of 87.0%. Among the surgical phases, the AI‐predicted duration of right‐sided greater curvature lymphadenectomy was shorter for surgeons with higher robotic case volume than those with lower volume (3789 vs. 2547 frames, p < 0.01), indicating phase‐specific differences in operative performance. Conclusions: The proposed AI model demonstrated robust performance in multicenter surgical phase recognition. AI‐based surgical phase analysis enabled objective characterization of phase‐specific operative profiles according to surgeon experience. These findings support the feasibility of surgical phase analysis as an exploratory framework for performance profiling and educational support in robotic gastrectomy. However, generalizability may be limited owing to institutional overlap between the training and validation datasets.
SN - 2475-0328
ER -