ArticleWorld journal of gastroenterology2020
Chronic atrophic gastritis detection with a convolutional neural network considering stomach regions.
Article in World journal of gastroenterology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- 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
- High expression of GRB14 is associated with cancer progression and poor prognosis in gastric cancer.Discover oncology · 2025Article
- A web-based pharmacological approach to the mechanism of action of Radix Bupleuri in the treatment of chronic atrophic gastritis.Cytotechnology · 2025Article
- Artificial Intelligence for the Automatic Diagnosis of Gastritis: A Systematic Review.Journal of clinical medicine · 2024Review
- Experimental Research on the Antitumor Effect of Human Gastric Cancer Cells Transplanted in Nude Mice Based on Deep Learning Combined with Spleen-Invigorating Chinese Medicine.Computational and mathematical methods in medicine · 2022Article
- Clinical efficacy ofAmerican journal of translational research · 2022Article
- Status quo and future prospects of artificial neural network from the perspective of gastroenterologists.World journal of gastroenterology · 2021Review
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4 authors.
Funding
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Abstract
backgroundThe risk of gastric cancer increases in patients with
aimTo evaluate the effectiveness of stomach regions that are automatically estimated by a deep learning-based model for CAG detection.
methodsWe used 815 gastric X-ray images (GXIs) obtained from 815 subjects. The ground truth of this study was the diagnostic results in X-ray and endoscopic examinations. For a part of GXIs for training, the stomach regions are manually annotated. A model for automatic estimation of the stomach regions is trained with the GXIs. For the rest of them, the stomach regions are automatically estimated. Finally, a model for automatic CAG detection is trained with all GXIs for training.
resultsIn the case that the stomach regions were manually annotated for only 10 GXIs and 30 GXIs, the harmonic mean of sensitivity and specificity of CAG detection were 0.955 ± 0.002 and 0.963 ± 0.004, respectively.
conclusionBy estimating stomach regions automatically, our method contributes to the reduction of the workload of manual annotation and the accurate detection of the CAG.
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