ArticleBMC gastroenterology2022
Deep learning model can improve the diagnosis rate of endoscopic chronic atrophic gastritis: a prospective cohort 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 20 papers, 1 of them a synthesis that pooled it.
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20 citing papers in PubMed, 1 synthesis or guideline pooled it, 26 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
- Knowledge, attitudes, and practices regarding chronic atrophic gastritis in gastroenterology outpatient patients.BMC public health · 2026Article
- Development and validation of a novel YOLOv5-based artificial intelligence model for gastric mucosal lesion detection.Surgical endoscopy · 2026Article
- Comparing artificial intelligence and healthcare professional performance in surgical and interventional video analysis: a systematic review and meta-analysis.NPJ digital medicine · 2026Article
- Towards deep-learning based detection and quantification of intestinal metaplasia on digitized gastric biopsies: a multi-expert comparative study.Scientific reports · 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
- Application of deep learning-based convolutional neural networks in gastrointestinal disease endoscopic examination.World journal of gastroenterology · 2025Review
- Update understanding on diagnosis and histopathological examination of atrophic gastritis: A review.World journal of gastrointestinal oncology · 2024Review
- Chronic Atrophic Gastritis and Intestinal Metaplasia: A Latin American Perspective.The Korean journal of helicobacter and upper gastrointestinal research · 2024Review
- Artificial Intelligence for the Automatic Diagnosis of Gastritis: A Systematic Review.Journal of clinical medicine · 2024Review
- Endoscopic Grading and Sampling of Gastric Precancerous Lesions: A Comprehensive Literature Review.Current oncology (Toronto, Ont.) · 2024Review
- Computer-aided diagnosis in real-time endoscopy for all stages of gastric carcinogenesis: Development and validation study.United European gastroenterology journal · 2024Article
- Clinical Decision Support System for All Stages of Gastric Carcinogenesis in Real-Time Endoscopy: Model Establishment and Validation Study.Journal of medical Internet research · 2023Article
- Focusing onFrontiers in cellular and infection microbiology · 2023Review
- 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
- Where do we stand in AI for endoscopic image analysis? Deciphering gaps and future directions.NPJ digital medicine · 2022Review
- Deep learning as a novel method for endoscopic diagnosis of chronic atrophic gastritis: a prospective nested case-control study.BMC gastroenterology · 2022Article
- Constructing and validating vision transformer-based assisted detection models for atrophic gastritis: A retrospective study.Science progressArticle
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2 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
aimsChronic atrophic gastritis (CAG) is a precancerous form of gastric cancer. However, with pathological diagnosis as the gold standard, the sensitivity of endoscopic diagnosis of atrophy is only 42%. We developed a deep learning (DL)-based real-time video monitoring diagnostic model for endoscopic CAG and conducted a prospective cohort study to verify whether this diagnostic model could improve the diagnosis rate of endoscopic CAG compared with that of endoscopists.
methodsA U-NET network was used to build a real-time video monitoring diagnostic model for endoscopic CAG based on DL. We enrolled 431 patients who underwent gastroscopy from October 1, 2020, to December 1, 2020. To keep the baseline data of enrolled patient uniform and control for confounding factors, we applied a paired design and included the same patients in both the DL and the endoscopist group.
resultsThe DL model improved the diagnosis rate of endoscopic CAG compared with that of endoscopists. Compared with diagnoses by endoscopists, the proportions of moderate and severe CAG in the atrophy patients diagnosed by the DL model were significantly larger, the proportion of "type O" CAG was significantly larger, the number of atrophy sites found was significantly increased, and the number of biopsies was significantly decreased. Compared with diagnoses by endoscopists, in the atrophic lesions diagnosed by the DL model, the proportions of severe atrophy and severe intestinal metaplasia were significantly increased.
conclusionsOur study suggested the DL model could improve the diagnosis rate of endoscopic CAG compared with that of endoscopists.
trial registrationChiCTR2100044458, 18/03/2020.
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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.