Evidence map›Paper›PMID 39815116›Full record

ArticleJournal of gastroenterology2025

Multistage deep learning for classification of Helicobacter pylori infection status using endoscopic images.

Guang Li, Ren Togo, Katsuhiro Mabe, Shunpei Nishida, Yoshihiro Tomoda, Fumiyuki Shiratani, Masashi Hirota, Takahiro Ogawa, Miki Haseyama

Abstract read
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Article in Journal of gastroenterology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. AI-powered three-categoryFrontiers in microbiology · 2025
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Guang LiEducation and Research Center for Mathematical and Data Science, Hokkaido University, Sapporo, Japan.
Ren TogoFaculty of Information Science and Technology, Hokkaido University, Sapporo, Japan.
Katsuhiro MabeMabe Goryokaku Gastrointestinal Endoscopy Clinic, Hakodate, Japan.
Shunpei NishidaOlympus Corporation, Tokyo, Japan.
Yoshihiro TomodaOlympus Medical Systems Corporation, Tokyo, Japan.
Fumiyuki ShirataniOlympus Medical Systems Corporation, Tokyo, Japan.
Masashi HirotaOlympus Medical Systems Corporation, Tokyo, Japan.
Takahiro OgawaFaculty of Information Science and Technology, Hokkaido University, Sapporo, Japan.
Miki HaseyamaFaculty of Information Science and Technology, Hokkaido University, Sapporo, Japan. mhaseyama@lmd.ist.hokudai.ac.jp.

Funding

Japan Society for the Promotion of Science JP23K11141Japan Society for the Promotion of Science JP23K21676Japan Society for the Promotion of Science JP24K02942Japan Society for the Promotion of Science JP24K23849
6 · The paper itself

Abstract

backgroundThe automated classification of Helicobacter pylori infection status is gaining attention, distinguishing among uninfected (no history of H. pylori infection), current infection, and post-eradication. However, this classification has relatively low performance, primarily due to the intricate nature of the task. This study aims to develop a new multistage deep learning method for automatically classifying H. pylori infection status.

methodsThe proposed multistage deep learning method was developed using a training set of 538 subjects, then tested on a validation set of 146 subjects. The classification performance of this new method was compared with the findings of four physicians.

resultsThe accuracy of our method was 87.7%, 83.6%, and 95.9% for uninfected, post-eradication, and currently infected cases, respectively, whereas that of the physicians was 81.7%, 76.5%, and 90.3%, respectively. When including the patient's H. pylori eradication history information, the classification accuracy of the method was 92.5%, 91.1%, and 98.6% for uninfected, post-eradication, and currently infected cases, respectively, whereas that of the physicians was 85.6%, 85.1%, and 97.4%, respectively.

conclusionThe new multistage deep learning method shows potential for an innovative approach to gastric cancer screening. It can evaluate individual subjects' cancer risk based on endoscopic images and reduce the burden of physicians.

Indexed as

Deep LearningHelicobacter InfectionsHelicobacter pyloriAdultAgedFemaleHumansMaleMiddle AgedStomach NeoplasmsEndoscopic imagesGastric cancerHelicobacter pylori infectionMultistage deep learning

Identifiers

PMID39815116

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.