AIMC Topic: Colonoscopy
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The use of artificial intelligence in colonoscopic evaluations.
Current opinion in gastroenterology
Oct 25, 2024
PURPOSE OF REVIEW: This review aims to highlight the transformative impact of artificial intelligence in the field of gastrointestinal endoscopy, particularly in the detection and characterization of colorectal polyps.
Colonoscopy polyp classification via enhanced scattering wavelet Convolutional Neural Network.
PloS one
Oct 11, 2024
Among the most common cancers, colorectal cancer (CRC) has a high death rate. The best way to screen for colorectal cancer (CRC) is with a colonoscopy, which has been shown to lower the risk of the disease. As a result, Computer-aided polyp classific...
Effectiveness of artificial intelligence assisted colonoscopy on adenoma and polyp miss rate: A meta-analysis of tandem RCTs.
Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver
Sep 24, 2024
BACKGROUND AND AIMS: One-fourth of colorectal neoplasia is missed at screening colonoscopy, representing the leading cause of interval colorectal cancer (I-CRC). This systematic review and meta-analysis summarizes the efficacy of computer-aided colon...
Effect of a novel artificial intelligence-based cecum recognition system on adenoma detection metrics in a screening colonoscopy setting.
Gastrointestinal endoscopy
Sep 18, 2024
BACKGROUND AND AIMS: Cecal intubation in colonoscopy relies on self-reporting. We developed an artificial intelligence-based cecum recognition system (AI-CRS) for post-hoc verification of cecal intubation and explored its impact on adenoma metrics.
Use of artificial intelligence improves colonoscopy performance in adenoma detection: a systematic review and meta-analysis.
Gastrointestinal endoscopy
Aug 30, 2024
BACKGROUND AND AIMS: Artificial intelligence (AI) is increasingly used to improve adenoma detection during colonoscopy. This meta-analysis aimed to provide an updated evaluation of computer-aided detection (CADe) systems and their impact on key colon...
Polyp detection with colonoscopy assisted by the GI Genius artificial intelligence endoscopy module compared with standard colonoscopy in routine colonoscopy practice (COLO-DETECT): a multicentre, open-label, parallel-arm, pragmatic randomised controlled trial.
The lancet. Gastroenterology & hepatology
Aug 14, 2024
BACKGROUND: Increased polyp detection during colonoscopy is associated with decreased post-colonoscopy colorectal cancer incidence and mortality. The COLO-DETECT trial aimed to assess the clinical effectiveness of the GI Genius intelligent endoscopy ...
AI support for colonoscopy quality control using CNN and transformer architectures.
BMC gastroenterology
Aug 9, 2024
BACKGROUND: Construct deep learning models for colonoscopy quality control using different architectures and explore their decision-making mechanisms.
Lesion-Decoupling-Based Segmentation With Large-Scale Colon and Esophageal Datasets for Early Cancer Diagnosis.
IEEE transactions on neural networks and learning systems
Aug 5, 2024
Lesions of early cancers often show flat, small, and isochromatic characteristics in medical endoscopy images, which are difficult to be captured. By analyzing the differences between the internal and external features of the lesion area, we propose ...
An Accurate and Automated Method for Adenoma Detection Rate and Report Card Generation Utilizing Common Electronic Health Records.
Journal of clinical gastroenterology
Aug 1, 2024
GOALS: To develop an automated method for Adenoma Detection Rate (ADR) calculation and report card generation using common electronic health records (EHRs).
Deep Learning Model Using Stool Pictures for Predicting Endoscopic Mucosal Inflammation in Patients With Ulcerative Colitis.
The American journal of gastroenterology
Jul 25, 2024
INTRODUCTION: Stool characteristics may change depending on the endoscopic activity of ulcerative colitis (UC). We developed a deep learning model using stool photographs of patients with UC (DLSUC) to predict endoscopic mucosal inflammation.
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