Evidence map›Paper›PMID 42387009›Full record

ArticleSurgical endoscopy2026

Impact of artificial intelligence intervention on awareness of anatomical landmarks in laparoscopic gastrectomy: An exploratory study.

Takumi Hasegawa, Tsuyoshi Etoh, Tomoki Nakano, Yoshimasa Aoyama, Yusuke Matsunobu, Reo Fujishima, Shunsuke Fujita, Yuki Shitomi, Tomonori Akagi, Shigeo Ninomiya and 3 more

Abstract read
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

13 authors.

Takumi HasegawaDepartment of Gastroenterological and Pediatric Surgery, Oita University Faculty of Medicine, Oita, Japan.
Tsuyoshi EtohDepartment of Gastroenterological and Pediatric Surgery, Oita University Faculty of Medicine, Oita, Japan. riekmk310@gmail.com.
Tomoki NakanoDepartment of Information Systems and Engineering, Faculty of Information Engineering, Fukuoka Institute of Technology, Fukuoka, Japan.
Yoshimasa AoyamaDepartment of Gastroenterological and Pediatric Surgery, Oita University Faculty of Medicine, Oita, Japan.
Yusuke MatsunobuDepartment of Healthcare AI Data Science, Oita University Faculty of Medicine, Oita, Japan.
Reo FujishimaDepartment of Gastroenterological and Pediatric Surgery, Oita University Faculty of Medicine, Oita, Japan.
Shunsuke FujitaDepartment of Gastroenterological and Pediatric Surgery, Oita University Faculty of Medicine, Oita, Japan.
Yuki ShitomiDepartment of Gastroenterological and Pediatric Surgery, Oita University Faculty of Medicine, Oita, Japan.
Tomonori AkagiDepartment of Gastroenterological and Pediatric Surgery, Oita University Faculty of Medicine, Oita, Japan.
Shigeo NinomiyaDepartment of Gastroenterological and Pediatric Surgery, Oita University Faculty of Medicine, Oita, Japan.
Yoshitake UedaDepartment of Comprehensive Surgery for Community Medicine, Oita University Faculty of Medicine, Oita, Japan.
Tatsushi TokuyasuDepartment of Information Systems and Engineering, Faculty of Information Engineering, Fukuoka Institute of Technology, Fukuoka, Japan.
Masafumi InomataDepartment of Gastroenterological and Pediatric Surgery, Oita University Faculty of Medicine, Oita, Japan.

Funding

Japan Society for the Promotion of Science JP24K11851
6 · The paper itself

Abstract

backgroundWe have developed gastric cancer/gastrectomy artificial intelligence (gAI) that leverages the tacit knowledge of experienced surgeons to visualize anatomical landmarks, including the pancreas and dimpling lines (DLs), to help prevent pancreatic fistula in laparoscopic gastrectomy for gastric cancer. However, whether gAI contributes to the awareness among surgeons remains unclear. Therefore, in the present study, we aimed to evaluate how gAI intervention influences anatomical landmark recognition, which reflects the awareness of these landmarks among surgeons, and how its effects vary by surgical expertise.

methodsAn external evaluation committee established the consensus reference standard and validated gAI. Twelve surgeons from four institutions were grouped by expertise as follows: junior trainee, senior trainee, and trainer. Each surgeon viewed videos of the infrapyloric and suprapancreatic areas without and with gAI, annotating images from each scene. Subjective recognition change rate and recognition score difference in anatomical landmark recognition compared to reference standards were assessed across expertise levels.

resultsIn the infrapyloric area, subjective recognition change rates were 52% for junior trainees, 38% for senior trainees, and 9% for trainers, with trainers exhibiting significantly lower rates than both trainee groups (p < 0.001 for both). In the suprapancreatic area, the corresponding rates were 38, 17, and 4%, respectively; trainers exhibited significantly lower rates than junior trainees (p < 0.001) but not senior trainees (p = 0.054). In exploratory analyses limited to observations with measured score changes, recognition scores for the DL between the mesogastrium and pancreas increased significantly in junior trainees in the suprapancreatic area (p < 0.01) and senior trainees in the infrapyloric area (p = 0.03).

conclusionsThis exploratory study suggests that gAI intervention could support awareness of anatomical landmark recognition in laparoscopic gastrectomy with differential effects across expertise levels, particularly among novice surgeons.

Indexed as

Anatomic LandmarksArtificial IntelligenceClinical CompetenceGastrectomyLaparoscopyStomach NeoplasmsAwarenessFemaleHumansAnatomical landmarkArtificial intelligenceAwarenessGastric cancerLaparoscopic gastrectomy

Identifiers

PMID42387009
PMCPMC13582402

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.