Evidence map›Paper›PMID 42509496›Full record

ArticleSurgical endoscopy2026

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

Ayumi Umemiya, Shin Takenaka, Daichi Kitaguchi, Shintaro Arakaki, Masashi Wakabayashi, Shuntaro Iuchi, Nao Baba, Risa Takai, Yoshitsugu Yanagida, Kimimasa Sasaki and 6 more

Abstract read
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In one paragraph

Article in Surgical endoscopy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Ayumi UmemiyaGastric Surgery Division, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
Shin TakenakaDepartment for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
Daichi KitaguchiDepartment for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
Shintaro ArakakiDepartment for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
Masashi WakabayashiBiostatistics Division, Center for Research Administration and Support, National Cancer Center, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.
Shuntaro IuchiDepartment for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
Nao BabaDepartment for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
Risa TakaiDepartment for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
Yoshitsugu YanagidaDepartment for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
Kimimasa SasakiDepartment for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
Norihito KosugiDepartment for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
Masahiro YuraGastric Surgery Division, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
Nobuyoshi TakeshitaDepartment for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
Takeo FujitaCourse of Advanced Clinical Research of Cancer, Juntendo University Graduate School of Medicine, 2-1-1, Hongo, Bunkyo-Ward, Tokyo, 113-8421, Japan.
Takahiro KinoshitaGastric Surgery Division, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
Masaaki ItoDepartment 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.ORCID http://orcid.org/0000-0002-1101-2707

Funding

Japan Agency for Medical Research and Development JP21vk0124001
6 · The paper itself

Abstract

backgroundTraditional 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.

methodsSurgical 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.

resultsA 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.

conclusionsThe 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.

Indexed as

Artificial IntelligenceClinical CompetenceGastrectomyLaparoscopyFeasibility StudiesHumansVideo RecordingDeep learningGastrectomyLaparoscopyProfessionalismSurgical

Identifiers

PMID42509496

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.