Evidence map›Paper›PMID 42023069›Full record

ArticleJournal of pathology informatics2026

Development of an artificial intelligence-based computer-aided detection system for routine gastric biopsy diagnosis.

Daiki Taniyama, Kazuya Kuraoka, Akihisa Saito, Aya Kido, Ayaka Shirai, Rie Yamamoto, Masayuki Mano, Norihiro Teramoto, Shiro Miura, Morihisa Akagi and 5 more

Abstract read
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In one paragraph

Article in Journal of pathology informatics, 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

15 authors.

Daiki TaniyamaDevelopmental Therapeutics Branch, Laboratory of Molecular Pharmacology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Kazuya KuraokaDepartment of Diagnostic Pathology, National Hospital Organization Kure Medical Center and Chugoku Cancer Center, Kure, Japan.
Akihisa SaitoDepartment of Diagnostic Pathology, National Hospital Organization Kure Medical Center and Chugoku Cancer Center, Kure, Japan.
Aya KidoDepartment of Diagnostic Pathology, National Hospital Organization Kure Medical Center and Chugoku Cancer Center, Kure, Japan.
Ayaka ShiraiDepartment of Diagnostic Pathology, National Hospital Organization Kure Medical Center and Chugoku Cancer Center, Kure, Japan.
Rie YamamotoDepartment of Diagnostic Pathology, National Hospital Organization Kure Medical Center and Chugoku Cancer Center, Kure, Japan.
Masayuki ManoDepartment of Clinical Laboratory and Surgical Pathology, National Hospital Organization Osaka National Hospital, Osaka, Japan.
Norihiro TeramotoDepartment of Pathology, National Hospital Organization Shikoku Cancer Center, Matsuyama, Japan.
Shiro MiuraDepartment of Diagnostic Pathology, National Hospital Organization Nagasaki Medical Center, Omura, Japan.
Morihisa AkagiDepartment of Internal Medicine, Hiroshima Memorial Hospital, Hiroshima, Japan.
Norihiko KatayamaDepartment of Internal Medicine, Kure Medical Association Hospital, Kure, Japan.
Tomonori KawasakiDepartment of Pathology, Saitama Medical University International Medical Center, Hidaka, Japan.
Chika NakajimaR&D Japan, Evident Corporation, Tokyo, Japan.
Susumu KikuchiR&D Japan, Evident Corporation, Tokyo, Japan.
Kiyomi TaniyamaDepartment of Diagnostic Pathology, National Hospital Organization Kure Medical Center and Chugoku Cancer Center, Kure, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To reflect real-world pathology practice, we developed an artificial intelligence-based pathological computer-aided detection system, trained on diverse epithelial and non-epithelial tumors for gastric biopsy specimens. A multicenter cohort comprising samples from six institutions was used for training and validated with an independent dataset from a seventh institution. We applied two distinct algorithms and three operational validity levels with optimized parameters to address the complexity of pathological diagnosis, reflecting routine diagnostic practice. Our system enabled the detection of malignant regions at low-magnification observation, aligning with the practical workflow of pathologists. In a reader study involving a limited number of test samples, the use of system assistance was associated with an improvement in diagnostic sensitivity. Further analysis revealed that samples with small and dispersed malignant foci had a higher rate of false-negative diagnoses, underscoring the potential of our system to improve diagnostic sensitivity. This study highlights the promise of integrating our system into real-world practice to aid pathologists in the routine diagnosis of gastric biopsy specimens.

Indexed as

Artificial intelligenceComputer-aided detection systemDigital pathologyGastric biopsy

Identifiers

PMID42023069
PMCPMC13098442

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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.