ArticleEBioMedicine2017
Application of Convolutional Neural Networks in the Diagnosis of Helicobacter pylori Infection Based on Endoscopic Images.
Article in EBioMedicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 118 papers, 6 of them syntheses that pooled it.
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Who cites it
118 citing papers in PubMed, 6 syntheses or guidelines pooled it.
- Artificial intelligence in gastric cancer: a systematic review of machine learning and deep learning applications.Abdominal radiology (New York) · 2026Pooled it
- Pooled it
- Novel endoscopic techniques for the diagnosis of gastricFrontiers in microbiology · 2024Pooled it
- Artificial Intelligence for the Prediction of Helicobacter Pylori Infection in Endoscopic Images: Systematic Review and Meta-Analysis Of Diagnostic Test Accuracy.Journal of medical Internet research · 2020Pooled it
- Artificial intelligence in gastric cancer: a systematic review.Journal of cancer research and clinical oncology · 2020Pooled it
- Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics.European journal of nuclear medicine and molecular imaging · 2019Pooled it
- Development and application of an artificial intelligence-assisted endoscopy system for diagnosis of Helicobacter pylori infection: a multicenter randomized controlled study.BMC gastroenterology · 2024Trial
- AI-assisted endoscopic detection of Helicobacter pylori infection in atrophic gastritis: a multicenter diagnostic study.Surgical endoscopy · 2026Article
- Automated diagnosis of Helicobacter pylori infection from routine endoscopic images using deep learning: a development and validation study.BMC gastroenterology · 2026Article
- Artificial Intelligence in Gastrointestinal Endoscopy and Hemostatic Decision-Making: Current Evidence, Clinical Implications and Implementation Barriers.Life (Basel, Switzerland) · 2026Review
- Integrated multi-task learning framework for hepatocellular carcinoma segmentation and histological grading using fused multi-phase MRI.Abdominal radiology (New York) · 2026Article
- Application of artificial intelligence for detection of Helicobacter pylori infection by magnetically controlled capsule endoscopy.Surgical endoscopy · 2026Article
- Multi-region and multi-image convolutional neural network model for detecting gastric helicobacter pylori infection.Surgical endoscopy · 2026Article
- Artificial Intelligence in Gastrointestinal Endoscopy: Current Advances, Clinical Integration, and Future Directions: A Narrative Review.Clinical and experimental gastroenterology · 2026Review
- Limited discriminative performance of endoscopic deep learning forFrontiers in gastroenterology (Lausanne, Switzerland) · 2026Article
- Subclass-Aware Contrastive Semi-Supervised Learning for Inflammatory Bowel Disease Classification from Colonoscopy Images.Bioengineering (Basel, Switzerland) · 2025Article
- Artificial Intelligence in Upper Gastrointestinal Endoscopy: Current Evidence, Practice, and Future Directions.Cureus · 2025Review
- Recent advancement in endoscopic diagnosis for risk stratification of gastric cancer.Clinical endoscopy · 2025Review
- Artificial intelligence-assisted endoscopic diagnosis system for diagnosing Helicobacter pylori infection: a multicenter study.BMC medicine · 2025Article
- Role of artificial intelligence in gastric diseases.World journal of gastroenterology · 2025Review
58 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
aimsThe role of artificial intelligence in the diagnosis of Helicobacter pylori gastritis based on endoscopic images has not been evaluated. We constructed a convolutional neural network (CNN), and evaluated its ability to diagnose H. pylori infection.
methodsA 22-layer, deep CNN was pre-trained and fine-tuned on a dataset of 32,208 images either positive or negative for H. pylori (first CNN). Another CNN was trained using images classified according to 8 anatomical locations (secondary CNN). A separate test data set (11,481 images from 397 patients) was evaluated by the CNN, and 23 endoscopists, independently.
resultsThe sensitivity, specificity, accuracy, and diagnostic time were 81.9%, 83.4%, 83.1%, and 198s, respectively, for the first CNN, and 88.9%, 87.4%, 87.7%, and 194s, respectively, for the secondary CNN. These values for the 23 endoscopists were 79.0%, 83.2%, 82.4%, and 230±65min (85.2%, 89.3%, 88.6%, and 253±92min by 6 board-certified endoscopists), respectively. The secondary CNN had a significantly higher accuracy than endoscopists (by 5.3%; 95% CI, 0.3-10.2).
conclusionH. pylori gastritis could be diagnosed based on endoscopic images using CNN with higher accuracy and in a considerably shorter time compared to manual diagnosis by endoscopists.
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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.