ArticleJournal of gastroenterology and hepatology2021
Application of artificial intelligence using a convolutional neural network for diagnosis of early gastric cancer based on magnifying endoscopy with narrow-band imaging.
Article in Journal of gastroenterology and hepatology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04676815 (Application of Narrow Band Imaging), which is not on this map. Cited by 74 papers, 7 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Application of Narrow Band Imaging (NBI) Under Electronic Bronchoscope in the Diagnosis and Staging of Lung Cancer
Who cites it
74 citing papers in PubMed, 7 syntheses or guidelines pooled it, 141 citations in OpenAlex.
- Comparative diagnostic accuracy of different artificial intelligence models for early gastric cancer: a systematic review and meta-analysis.Frontiers in oncology · 2025Pooled it
- Global research hotspots and trends in the application of artificial intelligence in gastric cancer: a bibliometric analysis from 2005 to 2024.Frontiers in oncology · 2025Pooled it
- The value of machine learning approaches in the diagnosis of early gastric cancer: a systematic review and meta-analysis.World journal of surgical oncology · 2024Pooled it
- Pooled it
- Artificial Intelligence-Assisted Endoscopic Diagnosis of Early Upper Gastrointestinal Cancer: A Systematic Review and Meta-Analysis.Frontiers in oncology · 2022Pooled it
- Current Evidence and Future Perspective of Accuracy of Artificial Intelligence Application for Early Gastric Cancer Diagnosis With Endoscopy: A Systematic and Meta-Analysis.Frontiers in medicine · 2021Pooled it
- Convolutional neural network-based artificial intelligence for the diagnosis of early esophageal cancer based on endoscopic images: A meta-analysis.Saudi journal of gastroenterology : official journal of the Saudi Gastroenterology AssociationPooled it
- Article
- Application of Artificial Intelligence in the Endoscopic Diagnosis of Gastric Cancer and Precancerous Lesions.Diagnostics (Basel, Switzerland) · 2026Review
- Development and validation of a novel YOLOv5-based artificial intelligence model for gastric mucosal lesion detection.Surgical endoscopy · 2026Article
- Dual-stream token fusion with Swin Transformer and lesion-aware tokens for gastric metaplasia classification in IoMT-assisted deployment.BMC medical imaging · 2026Article
- Usefulness of artificial intelligence in diagnosing early gastric cancer using magnifying endoscopy with narrow-band imaging.iGIE : innovation, investigation and insights · 2026Article
- Artificial Intelligence in Gastrointestinal Endoscopy and Hemostatic Decision-Making: Current Evidence, Clinical Implications and Implementation Barriers.Life (Basel, Switzerland) · 2026Review
- High-precision classification of WCE-based gastrointestinal abnormality using a fusion deep learning approach.Scientific reports · 2026Article
- World Endoscopy Organization Position Statements for Artificial Intelligence in Endoscopic Diagnosis of Gastric Epithelial Neoplasia.Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society · 2026Review
- Prevention of Gastric Cancer.Medicina (Kaunas, Lithuania) · 2026Review
- Real-World Evaluation of an AI-Assisted Diagnostic Support System for Early Gastric Cancer: Diagnostic Performance, Confidence Stratification, and Determinants of False-Positive Diagnosis.Journal of clinical medicine · 2026Article
- Application of AI-assisted magnifying colonoscopy system in the diagnosis of colorectal tumors: a multicenter exploratory diagnostic study.Surgical endoscopy · 2026Article
- Lightweight deep learning model for gastrointestinal precancerous lesion screening with attention enhancement.Frontiers in medicine · 2026Article
- Artificial intelligence-assisted diagnosis of early gastric cancer: present practice and future prospects.Annals of medicine · 2025Review
14 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
13 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
aimMagnifying endoscopy with narrow-band imaging (ME-NBI) has made a huge contribution to clinical practice. However, acquiring skill at ME-NBI diagnosis of early gastric cancer (EGC) requires considerable expertise and experience. Recently, artificial intelligence (AI), using deep learning and a convolutional neural network (CNN), has made remarkable progress in various medical fields. Here, we constructed an AI-assisted CNN computer-aided diagnosis (CAD) system, based on ME-NBI images, to diagnose EGC and evaluated the diagnostic accuracy of the AI-assisted CNN-CAD system.
methodsThe AI-assisted CNN-CAD system (ResNet50) was trained and validated on a dataset of 5574 ME-NBI images (3797 EGCs, 1777 non-cancerous mucosa and lesions). To evaluate the diagnostic accuracy, a separate test dataset of 2300 ME-NBI images (1430 EGCs, 870 non-cancerous mucosa and lesions) was assessed using the AI-assisted CNN-CAD system.
resultsThe AI-assisted CNN-CAD system required 60 s to analyze 2300 test images. The overall accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the CNN were 98.7%, 98%, 100%, 100%, and 96.8%, respectively. All misdiagnosed images of EGCs were of low-quality or of superficially depressed and intestinal-type intramucosal cancers that were difficult to distinguish from gastritis, even by experienced endoscopists.
conclusionsThe AI-assisted CNN-CAD system for ME-NBI diagnosis of EGC could process many stored ME-NBI images in a short period of time and had a high diagnostic ability. This system may have great potential for future application to real clinical settings, which could facilitate ME-NBI diagnosis of EGC in practice.
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Registered trials
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.