AIMC Topic: Gastritis
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A deep learning approach for gastroscopic manifestation recognition based on Kyoto Gastritis Score.
Annals of medicine
Nov 5, 2024
OBJECTIVE: The risk of gastric cancer can be predicted by gastroscopic manifestation recognition and the Kyoto Gastritis Score. This study aims to validate the applicability of AI approaches for recognizing gastroscopic manifestations according to th...
A novel endoscopic artificial intelligence system to assist in the diagnosis of autoimmune gastritis: a multicenter study.
Endoscopy
Oct 24, 2024
BACKGROUND: Autoimmune gastritis (AIG), distinct from Helicobacter pylori-associated atrophic gastritis (HpAG), is underdiagnosed due to limited awareness. This multicenter study aimed to develop a novel endoscopic artificial intelligence (AI) syste...
Identification of chronic non-atrophic gastritis and intestinal metaplasia stages in the Correa's cascade through machine learning analyses of SERS spectral signature of non-invasively-collected human gastric fluid samples.
Biosensors & bioelectronics
Jun 26, 2024
The progression of gastric cancer involves a complex multi-stage process, with gastroscopy and biopsy being the standard procedures for diagnosing gastric diseases. This study introduces an innovative non-invasive approach to differentiate gastric di...
Advancing Automatic Gastritis Diagnosis: An Interpretable Multilabel Deep Learning Framework for the Simultaneous Assessment of Multiple Indicators.
The American journal of pathology
May 17, 2024
The evaluation of morphologic features, such as inflammation, gastric atrophy, and intestinal metaplasia, is crucial for diagnosing gastritis. However, artificial intelligence analysis for nontumor diseases like gastritis is limited. Previous deep le...
Addressing diagnostic dilemmas in eosinophilic esophagitis using esophageal epithelial eosinophil-derived neurotoxin.
Journal of pediatric gastroenterology and nutrition
Dec 27, 2023
OBJECTIVES: Eosinophil-derived neurotoxin (EDN) is a viable marker of eosinophilic esophagitis (EoE) disease activity. We studied the utility of measuring EDN from esophageal epithelial brushings for diagnosing EoE, focusing on two scenarios: (1) cas...
Two-tiered deep-learning-based model for histologic diagnosis of Helicobacter gastritis.
Histopathology
Jul 31, 2023
AIMS: Helicobacter pylori (HP) infection is the most common cause of chronic gastritis worldwide. Due to the small size of HP and limited resolution, diagnosing HP infections is more difficult when using digital slides.
A Benchmark Dataset of Endoscopic Images and Novel Deep Learning Method to Detect Intestinal Metaplasia and Gastritis Atrophy.
IEEE journal of biomedical and health informatics
Jan 4, 2023
Endoscopy has been routinely used to diagnose stomach diseases including intestinal metaplasia (IM) and gastritis atrophy (GA). Such routine examination usually demands highly skilled radiologists to focus on a single patient with substantial time, c...
Deep learning for sensitive detection of Helicobacter Pylori in gastric biopsies.
BMC gastroenterology
Dec 11, 2020
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-morp...
Chronic gastritis classification using gastric X-ray images with a semi-supervised learning method based on tri-training.
Medical & biological engineering & computing
Mar 27, 2020
High-quality annotations for medical images are always costly and scarce. Many applications of deep learning in the field of medical image analysis face the problem of insufficient annotated data. In this paper, we present a semi-supervised learning ...
Deep learning in gastric tissue diseases: a systematic review.
BMJ open gastroenterology
Mar 26, 2020
BACKGROUND: In recent years, deep learning has gained remarkable attention in medical image analysis due to its capacity to provide results comparable to specialists and, in some cases, surpass them. Despite the emergence of deep learning research on...
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