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  3. Egid: A Comprehensive Multi-label Endoscopic Image Dataset For Gastritis Classification.

Related Experiment Video

Establishment and Evaluation of a Risk Prediction Model for Pathological Escalation of Gastric Low-Grade Intraepithelial Neoplasia
03:05

Establishment and Evaluation of a Risk Prediction Model for Pathological Escalation of Gastric Low-Grade Intraepithelial Neoplasia

Published on: February 16, 2024

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

Zeyu Li1, Yingying Fang1, Yiming Song1

  • 1Division 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.

Scientific Data
|June 22, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

A new dataset of endoscopic images aids in classifying chronic atrophic gastritis, a precancerous condition. This resource supports AI development for better gastritis diagnosis and cancer risk assessment.

Related Experiment Videos

Establishment and Evaluation of a Risk Prediction Model for Pathological Escalation of Gastric Low-Grade Intraepithelial Neoplasia
03:05

Establishment and Evaluation of a Risk Prediction Model for Pathological Escalation of Gastric Low-Grade Intraepithelial Neoplasia

Published on: February 16, 2024

Area of Science:

  • Gastroenterology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Chronic atrophic gastritis is a precancerous condition requiring accurate endoscopic classification.
  • Existing endoscopic datasets lack comprehensive multi-dimensional annotations for systematic gastritis classification.

Purpose of the Study:

  • To introduce the Endoscopic Gastritis Image Dataset (EGID), the first publicly available multi-label endoscopic gastritis dataset.
  • To enable the development of AI-based classification systems for gastritis.
  • To serve as an educational resource for clinical training in gastritis diagnosis and cancer risk stratification.

Main Methods:

  • Collected 5,883 white-light endoscopic images from 229 patients.
  • Annotated images across four dimensions: H. pylori infection status, presence of gastric atrophy, distribution of atrophy, and gastritis type.
  • Ensured high inter-rater agreement (Cohen's Kappa: 0.947-0.970) for image-based annotations through independent assessment and consensus adjudication.

    • Main Results:

    • The Endoscopic Gastritis Image Dataset (EGID) is now publicly available.
    • The dataset features high-quality, multi-dimensionally annotated endoscopic images.
    • Demonstrated excellent inter-rater reliability for endoscopic assessments.

    Conclusions:

    • EGID is the first multi-label endoscopic gastritis dataset, addressing a critical gap in existing resources.
    • The dataset will facilitate the advancement of AI in gastritis classification and diagnosis.
    • EGID will support clinical training and improve stratification of cancer risk in patients with gastritis.