AIMC Topic: Endoscopy, Gastrointestinal
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Artificial Intelligence and Medical Liability in Gastrointestinal Endoscopy.
Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association
Mar 30, 2024
Robustness evaluation of deep neural networks for endoscopic image analysis: Insights and strategies.
Medical image analysis
Mar 29, 2024
Computer-aided detection and diagnosis systems (CADe/CADx) in endoscopy are commonly trained using high-quality imagery, which is not representative for the heterogeneous input typically encountered in clinical practice. In endoscopy, the image quali...
A machine learning stacking model accurately estimating gastric fluid volume in patients undergoing elective sedated gastrointestinal endoscopy.
Postgraduate medicine
Mar 22, 2024
BACKGROUND: The current point-of-care ultrasound (POCUS) assessment of gastric fluid volume primarily relies on the traditional linear approach, which often suffers from moderate accuracy. This study aimed to develop an advanced machine learning (ML)...
Pediatric endoscopy: how can we improve patient outcomes and ensure best practices?
Expert review of gastroenterology & hepatology
Mar 12, 2024
INTRODUCTION: Strategies to promote high-quality endoscopy in children require consensus around pediatric-specific quality standards and indicators. Using a rigorous guideline development process, the international Pediatric Endoscopy Quality Improve...
Application of artificial intelligence in gastrointestinal endoscopy.
Arab journal of gastroenterology : the official publication of the Pan-Arab Association of Gastroenterology
Jan 15, 2024
Endoscopy is an important method for diagnosing gastrointestinal (GI) diseases. In this study, we provide an overview of the advances in artificial intelligence (AI) technology in the field of GI endoscopy over recent years, including esophagus, stom...
Artificial Intelligence in endoscopy: A future poll.
Arab journal of gastroenterology : the official publication of the Pan-Arab Association of Gastroenterology
Jan 13, 2024
Artificial Intelligence [AI] has been a trendy topic in recent years, with many developed medical applications. In gastrointestinal endoscopy, AI systems include computer-assisted detection [CADe] for lesion detection as bleedings and polyps and comp...
Efficacy of artificial intelligence in reducing miss rates of GI adenomas, polyps, and sessile serrated lesions: a meta-analysis of randomized controlled trials.
Gastrointestinal endoscopy
Jan 4, 2024
BACKGROUND AND AIMS: The aim of this study was to determine if utilization of artificial intelligence (AI) in the course of endoscopic procedures can significantly diminish both the adenoma miss rate (AMR) and the polyp miss rate (PMR) compared with ...
A newly developed deep learning-based system for automatic detection and classification of small bowel lesions during double-balloon enteroscopy examination.
BMC gastroenterology
Jan 2, 2024
BACKGROUND: Double-balloon enteroscopy (DBE) is a standard method for diagnosing and treating small bowel disease. However, DBE may yield false-negative results due to oversight or inexperience. We aim to develop a computer-aided diagnostic (CAD) sys...
A convenient machine learning model to predict full stomach and evaluate the safety and comfort improvements of preoperative oral carbohydrate in patients undergoing elective painless gastrointestinal endoscopy.
Annals of medicine
Dec 18, 2023
BACKGROUND AND AIMS: Assessment of the patient's gastric contents is the key to avoiding aspiration incidents, however, there is no effective method to determine whether elective painless gastrointestinal endoscopy (GIE) patients have a full stomach ...
Using deep learning to assess the function of gastroesophageal flap valve according to the Hill classification system.
Annals of medicine
Nov 10, 2023
BACKGROUND: The endoscopic Hill classification of the gastroesophageal flap valve (GEFV) is of great importance for understanding the functional status of the esophagogastric junction (EGJ). Deep learning (DL) methods have been extensively employed i...
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