ArticleJournal of gastroenterology2019
Detection of gastritis by a deep convolutional neural network from double-contrast upper gastrointestinal barium X-ray radiography.
Article in Journal of gastroenterology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.
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Who cites it
18 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence in gastric cancer: a systematic review.Journal of cancer research and clinical oncology · 2020Pooled it
- Deep learning in gastric tissue diseases: a systematic review.BMJ open gastroenterology · 2020Pooled it
- Assessing the Diagnostic Accuracy of BiomedCLIP for Detecting Contrast Use and Esophageal Strictures in Pediatric Radiography.Journal of clinical medicine · 2026Article
- Estimating the Amount of Air Inside the Stomach for Detecting Cancers on Gastric Radiographs Using Artificial Intelligence: an Observational, Cross-sectional Study.Journal of imaging informatics in medicine · 2025Observational
- Application of deep learning-based convolutional neural networks in gastrointestinal disease endoscopic examination.World journal of gastroenterology · 2025Review
- A Multitask Network for the Diagnosis of Autoimmune Gastritis.Journal of imaging · 2025Article
- Multistage deep learning for classification of Helicobacter pylori infection status using endoscopic images.Journal of gastroenterology · 2025Article
- Artificial Intelligence for the Automatic Diagnosis of Gastritis: A Systematic Review.Journal of clinical medicine · 2024Review
- Population-based X-ray gastric cancer screening in Hiroshima prefecture, Japan.World journal of clinical oncology · 2024Article
- Self-supervised learning for gastritis detection with gastric X-ray images.International journal of computer assisted radiology and surgery · 2023Article
- DeepHP: A New Gastric Mucosa Histopathology Dataset forInternational journal of molecular sciences · 2022Article
- Gastric Cancer Screening in Japan: A Narrative Review.Journal of clinical medicine · 2022Review
- Defect Detection of Subway Tunnels Using Advanced U-Net Network.Sensors (Basel, Switzerland) · 2022Article
- Development of a Deep Learning System to Detect Esophageal Cancer by Barium Esophagram.Frontiers in oncology · 2022Article
- Artificial intelligence in gastroenterology: A state-of-the-art review.World journal of gastroenterology · 2021Review
- Expert-level classification of gastritis by endoscopy using deep learning: a multicenter diagnostic trial.Endoscopy international open · 2021Article
- Chronic gastritis classification using gastric X-ray images with a semi-supervised learning method based on tri-training.Medical & biological engineering & computing · 2020Article
- A clinical metabolomics-based biomarker signature as an approach for early diagnosis of gastric cardia adenocarcinoma.Oncology letters · 2020Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
backgroundDeep learning has become a new trend of image recognition tasks in the field of medicine. We developed an automated gastritis detection system using double-contrast upper gastrointestinal barium X-ray radiography.
methodsA total of 6520 gastric X-ray images obtained from 815 subjects were analyzed. We designed a deep convolutional neural network (DCNN)-based gastritis detection scheme and evaluated the effectiveness of our method. The detection performance of our method was compared with that of ABC (D) stratification.
resultsSensitivity, specificity, and harmonic mean of sensitivity and specificity of our method were 0.962, 0.983, and 0.972, respectively, and those of ABC (D) stratification were 0.925, 0.998, and 0.960, respectively. Although there were 18 false negative cases in ABC (D) stratification, 14 of those 18 cases were correctly classified into the positive group by our method.
conclusionsDeep learning techniques may be effective for evaluation of gastritis/non-gastritis. Collaborative use of DCNN-based gastritis detection systems and ABC (D) stratification will provide more reliable gastric cancer risk information.
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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.