ArticleInternational journal of molecular sciences2020
Identification of Gastritis Subtypes by Convolutional Neuronal Networks on Histological Images of Antrum and Corpus Biopsies.
Article in International journal of molecular sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 25 citations in OpenAlex.
- Diagnosing and grading gastric atrophy and intestinal metaplasia using semi-supervised deep learning on pathological images: development and validation study.Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association · 2024Trial
- Emerging role of artificial intelligence in gastroenterology and hepatology.World journal of gastroenterology · 2025Review
- GastritisMIL: An interpretable deep learning model for the comprehensive histological assessment of chronic gastritis.Patterns (New York, N.Y.) · 2025Article
- Artificial Intelligence for the Automatic Diagnosis of Gastritis: A Systematic Review.Journal of clinical medicine · 2024Review
- Artificial intelligence in digital pathology: a systematic review and meta-analysis of diagnostic test accuracy.NPJ digital medicine · 2024Review
- Disease Diagnosis Based on Improved Gray Wolf Optimization (IGWO) and Ensemble Classification.Annals of biomedical engineering · 2023Article
- Immune-mediated Gastritis in a Patient with metastatic Lung Cancer due to Therapy with the immune Checkpoint Inhibitor Pembrolizumab - Differences and Similarities in Comparison to "endogenous" autoimmune Type A Gastritis and a review of literature.Zeitschrift fur Gastroenterologie · 2023Review
- Artificial Intelligence-The Rising Star in the Field of Gastroenterology and Hepatology.Diagnostics (Basel, Switzerland) · 2023Review
- Artificial intelligence: Emerging player in the diagnosis and treatment of digestive disease.World journal of gastroenterology · 2022Review
- Helicobacter pylori infection activates Wnt/β-catenin pathway to promote the occurrence of gastritis by upregulating ASCL1 and AQP5.Cell death discovery · 2022Article
- Deep learning for the detection of anatomical tissue structures and neoplasms of the skin on scanned histopathological tissue sections.Frontiers in oncology · 2022Article
- Role of artificial intelligence in multidisciplinary imaging diagnosis of gastrointestinal diseases.World journal of gastroenterology · 2021Review
- Artificial intelligence in gastroenterology and hepatology: Status and challenges.World journal of gastroenterology · 2021Review
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGastritis is a prevalent disease and commonly classified into autoimmune (A), bacterial (B), and chemical (C) type gastritis. While the former two subtypes are associated with an increased risk of developing gastric intestinal adenocarcinoma, the latter subtype is not. In this study, we evaluated the capability to classify common gastritis subtypes using convolutional neuronal networks on a small dataset of antrum and corpus biopsies.
methods1230 representative 500 × 500 µm images of 135 patients with type A, type B, and type C gastritis were extracted from scanned histological slides. Patients were allocated randomly into a training set (60%), a validation set (20%), and a test set (20%). One classifier for antrum and one classifier for corpus were trained and optimized. After optimization, the test set was analyzed using a joint result from both classifiers.
resultsOverall accuracy in the test set was 84% and was particularly high for type B gastritis with a sensitivity of 100% and a specificity of 93%.
conclusionsClassification of gastritis subtypes is possible using convolutional neural networks on a small dataset of histopathological images of antrum and corpus biopsies. Deep learning strategies to support routine diagnostic pathology merit further evaluation.
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