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Endoscopy
DOI: 10.1055/a-2451-3071
Original article

A novel endoscopic artificial intelligence system to assist in the diagnosis of autoimmune gastritis: a multicenter study

Shurong Chen‡   ‡
1   Department of Gastroenterology, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
,
Louzhe Xu‡   ‡
2   Institute of Biomedical Engineering, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
,
Lingling Yan
3   Department of Gastroenterology, Taizhou Hospital, Taizhou, China
,
Jie Zhang
1   Department of Gastroenterology, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
,
Xuefeng Zhou
1   Department of Gastroenterology, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
4   Department of Gastroenterology, the Second Hospital of Jiaxing, Jiaxing, China
,
Jiayi Wang
1   Department of Gastroenterology, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
5   Department of Gastroenterology, CHC international hospital, Ningbo, China
,
Tianlian Yan
1   Department of Gastroenterology, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
,
Jinghua Wang
1   Department of Gastroenterology, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
,
Xinjue He
1   Department of Gastroenterology, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
,
Han Ma
1   Department of Gastroenterology, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
,
Xuequn Zhang
1   Department of Gastroenterology, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
,
Shenghua Zhu
1   Department of Gastroenterology, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
,
Yizhen Zhang
1   Department of Gastroenterology, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
,
Chengfu Xu
1   Department of Gastroenterology, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
,
Jianguo Gao
1   Department of Gastroenterology, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
,
Xia Ji
4   Department of Gastroenterology, the Second Hospital of Jiaxing, Jiaxing, China
,
Dezhi Bai
6   Department of Gastroenterology, the First People’s hospital of Yuhang, Hangzhou, China
,
Yuan Chen
7   Department of Gastroenterology, the Third People’s hospital of Zhoushan, Zhoushan, China
,
Hongda Chen
8   Department of Gastroenterology, the Third Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
,
Yini Ke
9   Department of Rheumatology, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
,
Lan Li
1   Department of Gastroenterology, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
,
Chaohui Yu 
1   Department of Gastroenterology, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
,
Xinli Mao
3   Department of Gastroenterology, Taizhou Hospital, Taizhou, China
,
Ting Li∗   *
2   Institute of Biomedical Engineering, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
,
Yi Chen∗   *
1   Department of Gastroenterology, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
› Author AffiliationsSupported by: Key Research and Development Program of Zhejiang Province http://dx.doi.org/10.13039/100022963 2024C03202 to Yi Chen
Supported by: National Natural Science Foundation of Chinahttp://dx.doi.org/10.13039/501100001809 82170582 to Yi Chen
Supported by: Chinese Academy of Medical Science health innovation project 2021-I2M-1–042 to Ting Li
Supported by: Tianjin Outstanding Youth Fund Project 20JCJQIC00230 to Ting Li
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Abstract

Background Autoimmune gastritis (AIG), distinct from Helicobacter pylori-associated atrophic gastritis (HpAG), is underdiagnosed due to limited awareness. This multicenter study aimed to develop a novel endoscopic artificial intelligence (AI) system for assisting in AIG diagnosis.

Methods Patients diagnosed with AIG, HpAG, or nonatrophic gastritis (NAG), were retrospectively enrolled from six centers. Endoscopic images with relevant demographic and medical data were collected for development of the AI-assisted system based on a multi-site feature fusion model. The diagnostic performance of the AI model was evaluated in internal and external datasets. Endoscopists’ performance with and without AI support was tested and compared using Mann–Whitney U test. Heatmap analysis was performed to interpret AI model outputs.

Results 18 828 endoscopy images from 1070 patients (294 AIG, 386 HpAG, 390 NAG) were collected. On testing datasets, AI identified AIG with 96.9 % sensitivity, 92.2 % specificity, and area under the receiver operating characteristic curve (AUROC) of 0.990 (internal), and 90.3 % sensitivity, 93.1 % specificity, and AUROC of 0.973 (external). The performance of AI (sensitivity 91.3 %) was comparable to that of experts (87.3 %) and significantly outperformed nonexperts (70.0 %; P = 0.01). With AI support, the overall performance of endoscopists was improved (sensitivity 90.3 % [95 %CI 86.0 %–93.2 %] vs. 78.7 % [95 %CI 73.6 %–83.2 %]; P = 0.008). Heatmap analysis revealed consistent focus of AI on atrophic areas.

Conclusions This novel AI system demonstrated expert-level performance in identifying AIG and enhanced the diagnostic ability of endoscopists. Its application could be useful in guiding biopsy sampling and improving early detection of AIG.

‡ Co-first authors


* Co-senior authors


Supplementary Material



Publication History

Received: 14 May 2024

Accepted after revision: 24 October 2024

Accepted Manuscript online:
24 October 2024

Article published online:
29 November 2024

© 2024. Thieme. All rights reserved.

Georg Thieme Verlag KG
Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany

 

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