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BMC Gastroenterol ; 20(1): 417, 2020 Dec 11.
Artículo en Inglés | MEDLINE | ID: mdl-33308189

RESUMEN

BACKGROUND: Helicobacter 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. METHODS: We 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. RESULTS: With 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). CONCLUSION: Together, 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.


Asunto(s)
Aprendizaje Profundo , Gastritis , Infecciones por Helicobacter , Helicobacter pylori , Biopsia , Mucosa Gástrica , Gastritis/diagnóstico , Infecciones por Helicobacter/diagnóstico , Humanos
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