Evidence map›Paper›PMID 42331858›Full record

ArticleScientific data2026

EGID: A Comprehensive Multi-label Endoscopic Image Dataset for Gastritis Classification.

Zeyu Li, Yingying Fang, Yiming Song, Haotian Bai, Tianhao Li, Yaqi Wang, Qingwei Zhang, Xiaobo Li

Abstract readDataset
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
4 · The record

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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

8 authors.

Zeyu Li *Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Shanghai Institute of Digestive Disease, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Yingying Fang *Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Shanghai Institute of Digestive Disease, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Yiming Song *Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Shanghai Institute of Digestive Disease, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Haotian Bai *School of Media Engineering, Communication University of Zhejiang, Hangzhou, ZheJiang, China.
Tianhao LiSchool of Media Engineering, Communication University of Zhejiang, Hangzhou, ZheJiang, China.
Yaqi WangInnovation Center for Electronic Design Automation Technology, Hangzhou Dianzi University, Hangzhou, China. wangyaqi@hdu.edu.cn.
Qingwei ZhangDivision of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Shanghai Institute of Digestive Disease, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China. zhangqingweif@hotmail.com.
Xiaobo LiDivision of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Shanghai Institute of Digestive Disease, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China. lxb_1969@163.com.

Funding

Fundamental Research Funds for the Central Universities YG2025ZD01National Natural Science Foundation of China 82570787Noncommunicable Chronic Diseases-National Science and Technology Major Project 2024ZD0520800Science and Technology Commission of Shanghai Municipality 23Y11905300Shanghai Top-Priority Medical Center Construction Project 2023ZZ02022
6 · The paper itself

Abstract

Chronic atrophic gastritis is a well-recognized precancerous condition, emphasizing the need for accurate endoscopic classification. However, existing endoscopic datasets lack comprehensive multi-dimensional annotations for systematic gastritis classification. We present the Endoscopic Gastritis Image Dataset (EGID), comprising 5,883 high-quality white-light endoscopic images from 229 patients retrospectively collected at Renji Hospital, Shanghai, using the Olympus CV-290 system between March and November 2024. Each patient's images are systematically annotated across four independent clinical dimensions: H. pylori infection status, presence of gastric atrophy, distribution of atrophy, and gastritis type. H. pylori infection status was determined based on rapid urease test results, whereas the three image-based endoscopic dimensions were independently assessed by two experienced endoscopists, with consensus adjudication by a senior gastroenterologist for discordant cases. The image-based annotation dimensions showed excellent inter-rater agreement (Cohen's Kappa: 0.947-0.970). EGID provides the first publicly available multi-label endoscopic gastritis dataset, enabling development of artificial intelligence (AI)-based classification systems and serving as an educational resource for clinical training in gastritis diagnosis and cancer risk stratification.

Indexed as

GastritisGastritis, AtrophicHelicobacter InfectionsHelicobacter pyloriHumansRetrospective Studies

Identifiers

PMID42331858
PMCPMC13650140

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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.