Academic Journal
AI-based assessment of surgical efficiency and exposure quality in laparoscopic distal gastrectomy using a phase recognition model.
| Τίτλος: | AI-based assessment of surgical efficiency and exposure quality in laparoscopic distal gastrectomy using a phase recognition model. |
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| Συγγραφείς: | Umemiya A; Gastric Surgery Division, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.; Department for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.; Course of Advanced Clinical Research of Cancer, Juntendo University Graduate School of Medicine, 2-1-1, Hongo, Bunkyo-Ward, Tokyo, 113-8421, Japan., Takenaka S; Department for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan., Kitaguchi D; Department for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan., Arakaki S; Department for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan., Wakabayashi M; Biostatistics Division, Center for Research Administration and Support, National Cancer Center, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan., Iuchi S; Department for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan., Baba N; Department for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan., Takai R; Department for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan., Yanagida Y; Department for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan., Sasaki K; Department for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan., Kosugi N; Department for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan., Yura M; Gastric Surgery Division, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan., Takeshita N; Department for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan., Fujita T; Course of Advanced Clinical Research of Cancer, Juntendo University Graduate School of Medicine, 2-1-1, Hongo, Bunkyo-Ward, Tokyo, 113-8421, Japan.; Esophageal Surgery Division, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan., Kinoshita T; Gastric Surgery Division, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan., Ito M; Department for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan. maito@east.ncc.go.jp. |
| Πηγή: | Surgical endoscopy [Surg Endosc] 2026 Oct; Vol. 40 (10), pp. 8751-8762. Date of Electronic Publication: 2026 Jul 27. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Springer Country of Publication: Germany NLM ID: 8806653 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1432-2218 (Electronic) Linking ISSN: 09302794 NLM ISO Abbreviation: Surg Endosc Subsets: MEDLINE |
| Imprint Name(s): | Publication: 1992- : New York : Springer Original Publication: [Berlin] : Springer International, c1987- |
| Ιατρικοί όροι (MeSH): | Laparoscopy*/standards , Laparoscopy*/methods , Gastrectomy*/methods , Gastrectomy*/standards , Clinical Competence* , Artificial Intelligence*, Humans ; Video Recording ; Feasibility Studies |
| Περίληψη: | 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. (© 2026. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.) |
| Competing Interests: | Declarations. Disclosures: Drs. 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, and Masaaki Ito have no conflicts of interest or financial ties to disclose. |
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| Grant Information: | JP21vk0124001 Japan Agency for Medical Research and Development |
| Contributed Indexing: | Keywords: Deep learning; Gastrectomy; Laparoscopy; Professionalism; Surgical |
| Entry Date(s): | Date Created: 20260727 Date Completed: 20261002 Latest Revision: 20261008 |
| Update Code: | 20261008 |
| DOI: | 10.1007/s00464-026-13188-4 |
| PMID: | 42509496 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1432-2218 |
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| DOI: | 10.1007/s00464-026-13188-4 |