ArticleBMC gastroenterology2022
Deep learning as a novel method for endoscopic diagnosis of chronic atrophic gastritis: a prospective nested case-control study.
Article in BMC gastroenterology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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11 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.
- Diagnostic value of artificial intelligence-assisted endoscopy for chronic atrophic gastritis: a systematic review and meta-analysis.Frontiers in medicine · 2023Pooled it
- Artificial Intelligence for Endoscopic Diagnosis of Gastric Premalignant Lesions.The Korean journal of helicobacter and upper gastrointestinal research · 2026Review
- Comparing artificial intelligence and healthcare professional performance in surgical and interventional video analysis: a systematic review and meta-analysis.NPJ digital medicine · 2026Article
- Role of artificial intelligence in the detection and characterization of gastrointestinal premalignant and early malignant lesions.World journal of gastroenterology · 2025Review
- Diagnostic value of real-time computer-aided detection for precancerous lesion during esophagogastroduodenoscopy: A meta-analysis.World journal of gastrointestinal surgery · 2025Article
- Endoscopists and endoscopic assistants' qualifications, but not their biopsy rates, improve gastric precancerous lesions detection rate.World journal of gastrointestinal endoscopy · 2025Article
- Prospective Evaluation of Real-Time Artificial Intelligence for the Hill Classification of the Gastroesophageal Junction.United European gastroenterology journal · 2025Article
- Artificial Intelligence for the Automatic Diagnosis of Gastritis: A Systematic Review.Journal of clinical medicine · 2024Review
- Current study of pathogenetic mechanisms and therapeutics of chronic atrophic gastritis: a comprehensive review.Frontiers in cell and developmental biology · 2024Review
- U-Net deep learning model for endoscopic diagnosis of chronic atrophic gastritis and operative link for gastritis assessment staging: a prospective nested case-control study.Therapeutic advances in gastroenterology · 2023Article
- Constructing and validating vision transformer-based assisted detection models for atrophic gastritis: A retrospective study.Science progressArticle
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3 authors at 2 institutions in 1 country.
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Abstract
BACKGROUND AND
aimsChronic atrophic gastritis (CAG) is a precancerous disease that often leads to the development of gastric cancer (GC) and is positively correlated with GC morbidity. However, the sensitivity of the endoscopic diagnosis of CAG is only 42%. Therefore, we developed a real-time video monitoring model for endoscopic diagnosis of CAG based on U-Net deep learning (DL) and conducted a prospective nested case-control study to evaluate the diagnostic evaluation indices of the model and its consistency with pathological diagnosis.
methodsOur cohort consisted of 1539 patients undergoing gastroscopy from December 1, 2020, to July 1, 2021. Based on pathological diagnosis, patients in the cohort were divided into the CAG group or the chronic nonatrophic gastritis (CNAG) group, and we assessed the diagnostic evaluation indices of this model and its consistency with pathological diagnosis after propensity score matching (PSM) to minimize selection bias in the study.
resultsAfter matching, the diagnostic evaluation indices and consistency evaluation of the model were better than those of endoscopists [sensitivity (84.02% vs. 62.72%), specificity (97.04% vs. 81.95%), positive predictive value (96.60% vs. 77.66%), negative predictive value (85.86% vs. 68.73%), accuracy rate (90.53% vs. 72.34%), Youden index (81.06% vs. 44.67%), odd product (172.5 vs. 7.64), positive likelihood ratio (28.39 vs. 3.47), negative likelihood ratio (0.16 vs. 0.45), AUC (95% CI) [0.909 (0.884-0.934) vs. 0.740 (0.702-0.778)] and Kappa (0.852 vs. 0.558)].
conclusionsOur prospective nested case-control study proved that the diagnostic evaluation indices and consistency evaluation of the real-time video monitoring model for endoscopic diagnosis of CAG based on U-Net DL were superior to those of endoscopists. Trial registration ChiCTR2100044458 , 18/03/2020.
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