AI Medical Compendium Journal:
Journal of gastroenterology and hepatology

Showing 21 to 30 of 58 articles

Artificial intelligence for detecting superficial esophageal squamous cell carcinoma under multiple endoscopic imaging modalities: A multicenter study.

Journal of gastroenterology and hepatology Oct 4, 2021
BACKGROUND AND AIM: Diagnosis of esophageal squamous cell carcinoma (ESCC) is complicated and requires substantial expertise and experience. This study aimed to develop an artificial intelligence (AI) system for detecting superficial ESCC under multi...
Retrospective Studies Sensitivity and Specificity Esophageal Neoplasms Esophageal Squamous Cell Carcinoma Humans Artificial Intelligence
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Diagnostic performance of endoscopic ultrasound-artificial intelligence using deep learning analysis of gallbladder polypoid lesions.

Journal of gastroenterology and hepatology Aug 31, 2021
BACKGROUND AND AIM: Endoscopic ultrasound (EUS) is the most accurate diagnostic modality for polypoid lesions of the gallbladder (GB), but is limited by subjective interpretation. Deep learning-based artificial intelligence (AI) algorithms are under ...
Gallbladder Neoplasms Polyps Reproducibility of Results Deep Learning Endosonography Humans Artificial Intelligence
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Convolutional neural network-based object detection model to identify gastrointestinal stromal tumors in endoscopic ultrasound images.

Journal of gastroenterology and hepatology Aug 16, 2021
BACKGROUND AND AIM: We aimed to develop a convolutional neural network (CNN)-based object detection model for the discrimination of gastric subepithelial tumors, such as gastrointestinal stromal tumors (GISTs), and leiomyomas, in endoscopic ultrasoun...
Gastrointestinal Stromal Tumors Humans Neural Networks, Computer Reproducibility of Results Endosonography
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Artificial intelligence and polyp detection in colonoscopy: Use of a single neural network to achieve rapid polyp localization for clinical use.

Journal of gastroenterology and hepatology Aug 16, 2021
BACKGROUND AND AIM: Artificial intelligence has been extensively studied to assist clinicians in polyp detection, but such systems usually require expansive processing power, making them prohibitively expensive and hindering wide adaption. The curren...
Artificial Intelligence Algorithms Colonoscopy Humans Neural Networks, Computer Colonic Polyps
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Efficacy of an artificial neural network algorithm based on thick-slab magnetic resonance cholangiopancreatography images for the automated diagnosis of common bile duct stones.

Journal of gastroenterology and hepatology Jun 16, 2021
BACKGROUND AND AIM: Magnetic resonance cholangiopancreatography (MRCP) can accurately diagnose common bile duct (CBD) stones but is laborious to interpret. We developed an artificial neural network (ANN) capable of automatically assisting physicians ...
Neural Networks, Computer Algorithms Reproducibility of Results Common Bile Duct Humans Cholangiopancreatography, Magnetic Resonance
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Using machine-learning algorithms to identify patients at high risk of upper gastrointestinal lesions for endoscopy.

Journal of gastroenterology and hepatology May 10, 2021
BACKGROUND AND AIM: Endoscopic screening for early detection of upper gastrointestinal (UGI) lesions is important. However, population-based endoscopic screening is difficult to implement in populous countries. By identifying high-risk individuals fr...
Duodenal Diseases China Logistic Models Mass Screening Endoscopy, Gastrointestinal Patient Selection Esophageal Diseases Male Middle Aged Risk Assessment Female Adult Stomach Neoplasms Machine Learning Humans Algorithms Artificial Intelligence
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Artificial intelligence assists identifying malignant versus benign liver lesions using contrast-enhanced ultrasound.

Journal of gastroenterology and hepatology May 5, 2021
BACKGROUND AND AIM: This study aims to construct a strategy that uses assistance from artificial intelligence (AI) to assist radiologists in the identification of malignant versus benign focal liver lesions (FLLs) using contrast-enhanced ultrasound (...
Sensitivity and Specificity Ultrasonography Liver Neoplasms Humans Artificial Intelligence Contrast Media Magnetic Resonance Imaging
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Deep-learning system for real-time differentiation between Crohn's disease, intestinal Behçet's disease, and intestinal tuberculosis.

Journal of gastroenterology and hepatology Feb 20, 2021
BACKGROUND AND AIM: Pattern analysis of big data can provide a superior direction for the clinical differentiation of diseases with similar endoscopic findings. This study aimed to develop a deep-learning algorithm that performs differential diagnosi...
Neural Networks, Computer Female Young Adult Adult Male Middle Aged Humans Gastrointestinal Diseases Behcet Syndrome Tuberculosis, Gastrointestinal Diagnosis, Differential Colonoscopy Crohn Disease Deep Learning Enteritis Adolescent
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Early identification of patients with acute gastrointestinal bleeding using natural language processing and decision rules.

Journal of gastroenterology and hepatology Jan 25, 2021
BACKGROUND AND AIM: Guidelines recommend risk stratification scores in patients presenting with gastrointestinal bleeding (GIB), but such scores are uncommonly employed in practice. Automation and deployment of risk stratification scores in real time...
Gastrointestinal Hemorrhage Clinical Decision Rules Emergency Service, Hospital Acute Disease Early Diagnosis Humans Algorithms Electronic Health Records Risk Assessment Female Triage Male Natural Language Processing Middle Aged
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Application of artificial intelligence using a convolutional neural network for diagnosis of early gastric cancer based on magnifying endoscopy with narrow-band imaging.

Journal of gastroenterology and hepatology Jul 28, 2020
BACKGROUND AND AIM: Magnifying endoscopy with narrow-band imaging (ME-NBI) has made a huge contribution to clinical practice. However, acquiring skill at ME-NBI diagnosis of early gastric cancer (EGC) requires considerable expertise and experience. R...
Predictive Value of Tests Adult Stomach Neoplasms Endoscopy, Gastrointestinal Female Sensitivity and Specificity Humans Narrow Band Imaging Aged Male Neural Networks, Computer Aged, 80 and over Middle Aged Artificial Intelligence Early Detection of Cancer
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