Scholarlink

Background <p>Gastritis is defined as inflammation of the gastric mucosa often with erosive changes, and can be sub-grouped into acute versus chronic gastritis. <i>Helicobacter pylori</i>-associated gastritis (HepG) infection has a high baseline prevalence, roughly impacting 50% of patients in high-risk countries. Autoimmune gastritis (AIG) is significantly less common than HepG, and is estimated to be approximately 0.5–2%. Both HepG and AIG increase the risk of gastric adenocarcinoma. The aim of this study is to validate AI-driven models for images acquired through esophagogastroduodenoscopy (EGD) for early accurate diagnosis between AIG and HepG.</p> Method <p>In this retrospective study, 525 EGD images from two hospitals were collected and the clinical diagnosis of AIG and HepG was made by experienced gastroenterologists. And the deep learning model was constructed based on MobileNet V2. The model was compared with endoscopists in terms of AUC, accuracy, specificity, precision, and recall.</p> Result <p>AUC, accuracy, precision, recall and specificity were 0.81, 73.6%, 62.5%, 71.4%, and 75%, respectively, for AIG and HePG gastritis. The average performance of the two endoscopists was 0.641, 60.45%, 48.9%, 42.55% and 70.7%. The deep learning model also showed good performance in external independent validation sets.</p> Conclusion <p>Our study demonstrates the great potential of deep learning methods in identifying early AIG patients and as a tool for clinical AIDS. In the future, further prospective clinical studies are needed to evaluate the clinical applicability of this model.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Deep learning model based on gastroscopic images can differentiate between Helicobacter pylori gastritis and autoimmune gastritis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月18日

查看全文
  • Feng Yang,
  • Ning Xue,
  • Nuoyi Jiang,
  • Hao Chen,
  • Muhammad Ali,
  • Libardo Rueda Prada,
  • Joseph B. Parker,
  • Aya Elalfy,
  • Renee Morecroft-Phillipps,
  • Zhicong Chu,
  • Qianyu Guo,
  • Jianhua Tao

摘要

Background

Gastritis is defined as inflammation of the gastric mucosa often with erosive changes, and can be sub-grouped into acute versus chronic gastritis. Helicobacter pylori-associated gastritis (HepG) infection has a high baseline prevalence, roughly impacting 50% of patients in high-risk countries. Autoimmune gastritis (AIG) is significantly less common than HepG, and is estimated to be approximately 0.5–2%. Both HepG and AIG increase the risk of gastric adenocarcinoma. The aim of this study is to validate AI-driven models for images acquired through esophagogastroduodenoscopy (EGD) for early accurate diagnosis between AIG and HepG.

Method

In this retrospective study, 525 EGD images from two hospitals were collected and the clinical diagnosis of AIG and HepG was made by experienced gastroenterologists. And the deep learning model was constructed based on MobileNet V2. The model was compared with endoscopists in terms of AUC, accuracy, specificity, precision, and recall.

Result

AUC, accuracy, precision, recall and specificity were 0.81, 73.6%, 62.5%, 71.4%, and 75%, respectively, for AIG and HePG gastritis. The average performance of the two endoscopists was 0.641, 60.45%, 48.9%, 42.55% and 70.7%. The deep learning model also showed good performance in external independent validation sets.

Conclusion

Our study demonstrates the great potential of deep learning methods in identifying early AIG patients and as a tool for clinical AIDS. In the future, further prospective clinical studies are needed to evaluate the clinical applicability of this model.