ArticleBMC gastroenterology2020
Deep learning for sensitive detection of Helicobacter Pylori in gastric biopsies.
Article in BMC gastroenterology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Fusion of Texture Features Applied to H. pylori Infection Classification from Histopathological Images.Journal of imaging informatics in medicine · 2026Article
- Emerging role of artificial intelligence in gastroenterology and hepatology.World journal of gastroenterology · 2025Review
- Translational artificial intelligence in gastrointestinal and hepatic disorders: Advancing intelligent clinical decision-making for diagnosis, treatment, and prognosis.World journal of gastroenterology · 2025Review
- Detection ofJournal of imaging · 2025Article
- Rapid and Efficient Screening ofDiagnostics (Basel, Switzerland) · 2025Article
- Artificial intelligence-based model for diagnosingFrontiers in medicine · 2025Article
- Computational pathology in cancer diagnosis, prognosis, and prediction - present day and prospects.The Journal of pathology · 2023Review
- Artificial Intelligence-The Rising Star in the Field of Gastroenterology and Hepatology.Diagnostics (Basel, Switzerland) · 2023Review
- Review
- Use of digital pathology and artificial intelligence for the diagnosis of Helicobacter pylori in gastric biopsies.Pathologica · 2022Article
- Machine Learning for Future Subtyping of the Tumor Microenvironment of Gastro-Esophageal Adenocarcinomas.Cancers · 2021Review
- Shifting Gears in Precision Oncology-Challenges and Opportunities of Integrative Data Analysis.Biomolecules · 2021Review
- Role of artificial intelligence in multidisciplinary imaging diagnosis of gastrointestinal diseases.World journal of gastroenterology · 2021Review
- State of machine and deep learning in histopathological applications in digestive diseases.World journal of gastroenterology · 2021Review
- Data driven precision medicine: who is the driver?Oncotarget · 2021Article
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Authors and funding
8 authors.
Funding
Abstract
backgroundHelicobacter pylori, a 2 × 1 μm spiral-shaped bacterium, is the most common risk factor for gastric cancer worldwide. Clinically, patients presenting with symptoms of gastritis, routinely undergo gastric biopsies. The following histo-morphological evaluation dictates therapeutic decisions, where antibiotics are used for H. pylori eradication. There is a strong rational to accelerate the detection process of H. pylori on histological specimens, using novel technologies, such as deep learning.
methodsWe designed a deep-learning-based decision support algorithm that can be applied on regular whole slide images of gastric biopsies. In detail, we can detect H. pylori both on Giemsa- and regular H&E stained whole slide images.
resultsWith the help of our decision support algorithm, we show an increased sensitivity in a subset of 87 cases that underwent additional PCR- and immunohistochemical testing to define a sensitive ground truth of HP presence. For Giemsa stained sections, the decision support algorithm achieved a sensitivity of 100% compared to 68.4% (microscopic diagnosis), with a tolerable specificity of 66.2% for the decision support algorithm compared to 92.6 (microscopic diagnosis).
conclusionTogether, we provide the first evidence of a decision support algorithm proving as a sensitive screening option for H. pylori that can potentially aid pathologists to accurately diagnose H. pylori presence on gastric biopsies.
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