Evidence map›Paper›PMID 37592004›Full record

ArticleScientific reports2023

Application of artificial intelligence in endoscopic image analysis for the diagnosis of a gastric cancer pathogen-Helicobacter pylori infection.

Chih-Hsueh Lin, Ping-I Hsu, Chin-Dar Tseng, Pei-Ju Chao, I-Ting Wu, Supratip Ghose, Chih-An Shih, Shen-Hao Lee, Jia-Hong Ren, Chang-Bih Shie and 1 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
9.8field-weighted citation impact, top 1% of its field
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

15 citing papers in PubMed, 31 citations in OpenAlex.

  1. CurrentJGH open : an open access journal of gastroenterology and hepatology · 2026
    Review
  2. Rapid diagnosis ofFrontiers in public health · 2026
    Article
  3. Review
  4. Article
  5. Artificial Intelligence in Upper Gastrointestinal Diagnosis.The Korean journal of helicobacter and upper gastrointestinal research · 2025
    Review
  6. Review
  7. Article
  8. Article
  9. Review
  10. Review
  11. Article
  12. Review
  13. Article
  14. Applications of artificial intelligence in gastroscopy: a narrative review.The Journal of international medical research · 2024
    Review
  15. Article
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

11 authors at 5 institutions in 1 country.

Chih-Hsueh Lin *Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, 80778, Taiwan.
Ping-I Hsu *Division of Gastroenterology, Department of Medicine, An Nan Hospital, China Medical University, Tainan, Taiwan.
Chin-Dar TsengDepartment of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, 80778, Taiwan. a1102405111@gmail.com.
Pei-Ju ChaoDepartment of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, 80778, Taiwan.
I-Ting WuDivision of Gastroenterology, Department of Medicine, An Nan Hospital, China Medical University, Tainan, Taiwan.
Supratip GhoseDepartment of Education and Research, An Nan Hospital, China Medical University, Tainan, Taiwan.
Chih-An ShihDivision of Gastroenterology and Hepatology, Department of Internal Medicine, Antai Medical Care Corporation, Antai Tian-Sheng Memorial Hospital, Donggan, Pingtung County, Taiwan.
Shen-Hao LeeDepartment of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, 80778, Taiwan.
Jia-Hong RenDepartment of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, 80778, Taiwan.
Chang-Bih ShieDivision of Gastroenterology, Department of Medicine, An Nan Hospital, China Medical University, Tainan, Taiwan.
Tsair-Fwu LeeDepartment of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, 80778, Taiwan. tsairfwulee@gmail.com.
National Kaohsiung University of Science and TechnologyAn-Nan Hospital · TWChang Gung University · TWChina Medical University · TWMeiho University · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Helicobacter pylori (H. pylori) infection is the principal cause of chronic gastritis, gastric ulcers, duodenal ulcers, and gastric cancer. In clinical practice, diagnosis of H. pylori infection by a gastroenterologists' impression of endoscopic images is inaccurate and cannot be used for the management of gastrointestinal diseases. The aim of this study was to develop an artificial intelligence classification system for the diagnosis of H. pylori infection by pre-processing endoscopic images and machine learning methods. Endoscopic images of the gastric body and antrum from 302 patients receiving endoscopy with confirmation of H. pylori status by a rapid urease test at An Nan Hospital were obtained for the derivation and validation of an artificial intelligence classification system. The H. pylori status was interpreted as positive or negative by Convolutional Neural Network (CNN) and Concurrent Spatial and Channel Squeeze and Excitation (scSE) network, combined with different classification models for deep learning of gastric images. The comprehensive assessment for H. pylori status by scSE-CatBoost classification models for both body and antrum images from same patients achieved an accuracy of 0.90, sensitivity of 1.00, specificity of 0.81, positive predictive value of 0.82, negative predicted value of 1.00, and area under the curve of 0.88. The data suggest that an artificial intelligence classification model using scSE-CatBoost deep learning for gastric endoscopic images can distinguish H. pylori status with good performance and is useful for the survey or diagnosis of H. pylori infection in clinical practice.

Indexed as

Helicobacter InfectionsHelicobacter pyloriStomach NeoplasmsArtificial IntelligenceEndoscopyHumans

Identifiers

PMID37592004
PMCPMC10435453
OpenAlexW4385954353

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.