Evidence map›Paper›PMID 42394885›Full record

ArticleiGIE : innovation, investigation and insights2026

Usefulness of artificial intelligence in diagnosing early gastric cancer using magnifying endoscopy with narrow-band imaging.

Kenshi Yao, Shunpei Nishida, Masaki Miyaoka, Yoichiro Ono, Takao Kanemitsu, Kentaro Imamura, Satoshi Ishikawa, Rino Hasegawa, Shou Aso, Keisuke Takano and 6 more

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In one paragraph

Article in iGIE : innovation, investigation and insights, 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.

Kenshi YaoDepartment of Endoscopy, Fukuoka University Chikushi Hospital, Chikushino, Japan.
Shunpei NishidaAI Technology and Solution, Olympus Corp, Hachioji, Japan.
Masaki MiyaokaDepartment of Endoscopy, Fukuoka University Chikushi Hospital, Chikushino, Japan.
Yoichiro OnoDepartment of Gastroenterology, Fukuoka University Chikushi Hospital, Chikushino, Japan.
Takao KanemitsuDepartment of Gastroenterology, Fukuoka University Chikushi Hospital, Chikushino, Japan.
Kentaro ImamuraDepartment of Endoscopy, Fukuoka University Chikushi Hospital, Chikushino, Japan.
Satoshi IshikawaDepartment of Gastroenterology, Fukuoka University Chikushi Hospital, Chikushino, Japan.
Rino HasegawaDepartment of Gastroenterology, Fukuoka University Chikushi Hospital, Chikushino, Japan.
Shou AsoDepartment of Gastroenterology, Fukuoka University Chikushi Hospital, Chikushino, Japan.
Keisuke TakanoDepartment of Endoscopy, Fukuoka University Chikushi Hospital, Chikushino, Japan.
Shugo OzonoDepartment of Gastroenterology, Fukuoka University Chikushi Hospital, Chikushino, Japan.
Takayuki HiraseDepartment of Gastroenterology, Fukuoka University Chikushi Hospital, Chikushino, Japan.
Takashi HisabeDepartment of Gastroenterology, Fukuoka University Chikushi Hospital, Chikushino, Japan.
Seiichiro SakaguchiAI Technology and Solution, Olympus Corp, Hachioji, Japan.
Manabu IchikawaAdvanced Image Processing Technology, Olympus Medical Systems Corp, Hachioji, Japan.
Hidetoshi NishimuraAI Technology and Solution, Olympus Corp, Hachioji, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and Aims: In this study, we developed an artificial intelligence (AI) system to support the diagnosis of early gastric cancer using magnifying endoscopy with narrow-band imaging (NBI), with the goal of improving the diagnostic accuracy of upper gastrointestinal endoscopy. This study aimed to evaluate the diagnostic performance of the AI-based system compared with endoscopists. Methods: A total of 500 cases, including early gastric cancer and noncancer cases, were used to develop the diagnostic support AI system. We evaluated the AI system with models created using Results: The median (interquartile range [IQR]) sensitivity for the AI system and all endoscopists was 100% (100%-100%) and 76.8% (65.5%-78.9%), respectively. The sensitivity of the AI system was significantly higher than that of the endoscopists ( Conclusions: This study demonstrates the feasibility of applying the diagnostic support AI system during magnified observation with NBI and suggests that it could enhance the diagnostic accuracy of upper gastrointestinal endoscopy for early gastric cancer.

Identifiers

PMID42394885
PMCPMC13323892

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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.