Evidence map›Paper›PMID 41535450›Full record

ArticleSurgical endoscopy2026

Effect of Helicobacter pylori infection on deep learning-assisted detection of gastric neoplastic lesions under white light endoscopy.

Lingling Yan, Liangmin Zhang, Renquan Luo, Jiacheng Li, Weixia Wu, Weidan Wu, Zhenzhen Wang, Jinbang Peng, Haideng Yang, Binbin Gu and 1 more

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Article in Surgical endoscopy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

11 authors.

Lingling Yan *Department of Gastroenterology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, 150 Ximen Street, Linhai, Zhejiang, China.
Liangmin Zhang *Department of Pathology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, Zhejiang, China.
Renquan LuoDepartment of Gastroenterology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, 150 Ximen Street, Linhai, Zhejiang, China.
Jiacheng LiDepartment of Gastroenterology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, 150 Ximen Street, Linhai, Zhejiang, China.
Weixia WuDepartment of Emergency Medicine, The Second People's Hospital of Linhai, Linhai, Zhejiang, China.
Weidan WuDepartment of Gastroenterology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, 150 Ximen Street, Linhai, Zhejiang, China.
Zhenzhen WangDepartment of Gastroenterology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, 150 Ximen Street, Linhai, Zhejiang, China.
Jinbang PengDepartment of Gastroenterology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, 150 Ximen Street, Linhai, Zhejiang, China.
Haideng YangDepartment of Gastroenterology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, 150 Ximen Street, Linhai, Zhejiang, China.
Binbin GuDepartment of Gastroenterology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, 150 Ximen Street, Linhai, Zhejiang, China. 6570@enzemed.com.
Xinli MaoDepartment of Gastroenterology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, 150 Ximen Street, Linhai, Zhejiang, China. maoxl@enzemed.com.ORCID http://orcid.org/0000-0002-1975-1862

Funding

'Pioneer' and 'Leading Goose' R&D Program of Zhejiang 2025C02139Science and Technology Plan Project of Taizhou 22ywb09Science and Technology Plan Project of Taizhou 25ywb28Scientific Research Fund of Enze Medical Center 2025EZZD02
6 · The paper itself

Abstract

backgroundArtificial intelligence (AI)-assisted endoscopy facilitates upper gastrointestinal lesion detection. Whether Helicobacter pylori (H. pylori) infection influences its diagnostic performance remains unclear. This study evaluated the effect of H. pylori infection on an AI model's accuracy for diagnosing gastric neoplasms.

methodsA deep convolutional neural network-based AI system was evaluated for gastric neoplasm detection (low-grade intraepithelial neoplasia (LGIN), high-grade intraepithelial neoplasia (HGIN), and early gastric cancer (EGC)) in a retrospective cohort study. White light endoscopy (WLE) images (n = 2347) were collected from 563 patients who underwent imaging from November 2019 to August 2024 at Taizhou Hospital of Zhejiang Province to assess H. pylori infection's impact on diagnostic performance. Additional WLE images (n = 447) from 117 patients (September 2024-June 2025) were used to compare the AI system's performance with that of expert and non-expert endoscopists.

resultsThe AI system achieved 85.0% accuracy, 82.0% sensitivity, 87.6% specificity, 85.2% positive predictive value (PPV), and 84.8% negative predictive value (NPV). The accuracy (87.1% vs. 80.2%), specificity (89.9% vs. 76.6%), and NPV (89.7% vs. 65.4%) were significantly higher in the H. pylori-negative group than in the H. pylori-positive group (all P < 0.001), whereas the PPV was lower (82.7% vs. 88.5%, P = 0.008), with a comparable sensitivity (82.3% vs. 81.6%, P = 0.790). Within the H. pylori-negative cohort, further stratification into never-infected and eradicated subgroups showed that the eradicated group had significantly higher accuracy, sensitivity, and NPV than the H. pylori-positive group (all P < 0.05). It exhibited significantly higher accuracy for detecting non-neoplastic lesions and LGIN in the H. pylori-negative group (P < 0.05), but not for HGIN or EGC (P > 0.05). Its diagnostic accuracy was comparable to the expert endoscopists' (84.8% vs. 81.9%, P = 0.247) and significantly higher than the non-expert endoscopists' (84.8% vs. 72.1%, P < 0.001).

conclusionThis AI system exhibited excellent performance for gastric neoplasm detection, which was significantly affected by H. pylori infection (particularly for non-neoplastic lesions and LGIN). Eradication of H. pylori appeared to restore the diagnostic performance of the AI system. Its diagnostic accuracy in still image classification was comparable to expert endoscopists and superior to non-experts, supporting its potential as an adjunctive clinical endoscopy tool.

Indexed as

Deep LearningGastroscopyHelicobacter InfectionsHelicobacter pyloriStomach NeoplasmsAgedConvolutional Neural NetworksFemaleHumansIntelligent SystemsMaleMiddle AgedRetrospective StudiesSensitivity and SpecificityArtificial intelligenceDeep learningGastric neoplastic lesionHelicobacter pyloriWhite light endoscopy

Identifiers

PMID41535450

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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.