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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月22日

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

摘要

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.