AIMC Topic: Cathartics
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Applying machine learning to predict bowel preparation adequacy in elderly patients for colonoscopy: development and validation of a web-based prediction tool.
Annals of medicine
Mar 11, 2025
BACKGROUND: Adequate bowel preparation is crucial for effective colonoscopy, especially in elderly patients who face a high risk of inadequate preparation. This study develops and validates a machine learning model to predict bowel preparation adequa...
A clinical pilot trial of an artificial intelligence-driven smart phone application of bowel preparation for colonoscopy: a randomized clinical trial.
Scandinavian journal of gastroenterology
Dec 22, 2024
BACKGROUND: High-quality bowel preparation is paramount for a successful colonoscopy. This study aimed to explore the effect of artificial intelligence-driven smartphone software on the quality of bowel preparation.
Bowel preparation before elective right colectomy: Multitreatment machine-learning analysis on 2,617 patients.
Surgery
Sep 25, 2024
BACKGROUND: In the worldwide, real-life setting, some candidates for right colectomy still receive no bowel preparation, some receive oral antibiotics alone, some receive mechanical bowel preparation alone, and some receive mechanical bowel preparati...
Smartphone application for artificial intelligence-based evaluation of stool state during bowel preparation before colonoscopy.
Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society
Jun 21, 2024
OBJECTIVES: Colonoscopy (CS) is an important screening method for the early detection and removal of precancerous lesions. The stool state during bowel preparation (BP) should be properly evaluated to perform CS with sufficient quality. This study ai...
Automatic assessment of bowel preparation by an artificial intelligence model and its clinical applicability.
Journal of gastroenterology and hepatology
May 20, 2024
BACKGROUND AND AIM: Reliable bowel preparation assessment is important in colonoscopy. However, current scoring systems are limited by laborious and time-consuming tasks and interobserver variability. We aimed to develop an artificial intelligence (A...
The Machine Learning Model for Predicting Inadequate Bowel Preparation Before Colonoscopy: A Multicenter Prospective Study.
Clinical and translational gastroenterology
May 1, 2024
INTRODUCTION: Colonoscopy is a critical diagnostic tool for colorectal diseases; however, its effectiveness depends on adequate bowel preparation (BP). This study aimed to develop a machine learning predictive model based on Chinese adults for inadeq...
Validation of artificial intelligence-based bowel preparation assessment in screening colonoscopy (with video).
Gastrointestinal endoscopy
Apr 16, 2024
BACKGROUND AND AIMS: Accurate bowel preparation assessment is essential for determining colonoscopy screening intervals. Patients with suboptimal bowel preparation are at a high risk of missing >5 mm adenomas and should undergo an early repeat colono...
Artificial intelligence for the assessment of bowel preparation.
Gastrointestinal endoscopy
Dec 8, 2021
BACKGROUND AND AIMS: A reliable assessment of bowel preparation is important to ensure high-quality colonoscopy. Current bowel preparation scoring systems are limited by interobserver variability. This study aimed to demonstrate objective assessment ...
MOST: most-similar ligand based approach to target prediction.
BMC bioinformatics
Mar 11, 2017
BACKGROUND: Many computational approaches have been used for target prediction, including machine learning, reverse docking, bioactivity spectra analysis, and chemical similarity searching. Recent studies have suggested that chemical similarity searc...
Development and validation of the Open-Source Automatic Bowel Preparation Scale.
Gastrointestinal endoscopy
Jun 1, 2025
BACKGROUND AND AIMS: Insufficient bowel preparation accounts for up to 42% of missed adenomas in colonoscopy. However, major analysis programs found no correlation between adenoma detection rate and the human-rated Boston Bowel Preparation Scale (BBP...
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