View clinical trials related to Adenoma Colon.
Filter by:The endoscopist performances in the optical diagnosis (OD) of colonic polyps with the available technologies vary widely across centers and across endoscopists. The OD process is strictly related to the operator training and expertise. Most of the available studies in optical characterization have been carried out by expert endoscopist in tertiary high volume centers, and weren't replied on large unselected populations. For these reasons, at the moment the optical characterization of polypoid lesions can't replace, in the everyday clinical practice, the histopathological evaluation of resected polyps. Artificial intelligence (AI)-based systems have the potential to make optical characterization process of colonic polyps easier and more reliable, thus supporting the endoscopist in the application of leave-in-situ and of resect-and-discard strategies. The implementation of such strategies would lead to a significant economic saving and a decrease of risks and complications related to unnecessary polypectomy. GI-Genius System (Medtronic Inc, Minneaopolis, USA) is a CNN-based algorithm allowing an automatic OD of colonic polyps. This system does not require dedicated light setting for polyp evaluation as it works with white light high definition images, which are the actual standard in every endoscopic unit. During colonoscopy, when a polyp is framed within the screen, a green detection box surrounds the polyp and the system automatically provides (whenever possible) the optical diagnosis labeling the polyp as "adenoma or non-adenoma". When the automatic polyp charaterization is unfeasible the label "no prediction" appears. Nowadays only few data about the feasibility and performances of this system in clinical practice are available. In addition published studies are mostly focused on technical rather thann clinical issues. The present prospective observational trial is primarily aimed at evaluating the diagnostic accuracy of optical characterization of colonic polyps <= 1 cm using GI-Genius System in daily clinical practice, having histopathology examination as reference standard.
Adenoma detection rate (ADR) is a validated quality metric for colonoscopy with higher ADR correlated with improved colorectal cancer outcomes. Artificial intelligence (AI) can automatically detect polyps on the video monitor which may allow endoscopists in training to improve their ADR. Objective and Purpose of the study: Measure the effect of AI in a prospective, randomized manner to determine its impact on ADR of Gastroenterology trainees.
Colonoscopy completion by caecal intubation seldom represents a significant effort for the endoscopist. In this situation, additional techniques are necessary to achieve this goal: patients' manual abdominal compression, postural changes, and endoscopist relay. To date, no tool allows colonoscopy technical difficulty grading. This study pursues to describe the frequency of additional techniques for caecal intubation in a large sample of Argentinians in different centres who undergo colonoscopy for attending purposes, to develop a novel score for assessing colonoscopy technical difficulty.
A randomized, controlled study investigating the potential benefits of artificial intelligence (AI) in the detection of colonic polyps during outpatient colonoscopy. Randomization between the use of AI and no AI is performed before the study procedure.
A Prospective Randomized Comparison of colonoscopy Adenoma Detection Yield of (i) Standard Colonoscopy (SC), (ii) artificial intelligence (Discovery) aided colonoscopy, and (iii) artificial intelligence (Discovery) and permanently mounted balloon (G-EYE®) aided colonoscopy.
Retrospective study, single blind (patient), allowing a posteriori clinical data collection of 90 patients during their passage to the ambulatory endoscopy circuit, to consider 3 groups and thus to deduce a colonic adenoma detection rate for each arm : - Colonoscopy Only Group - Artificial intelligence only group (IA GI GENIUS ™ alone) - Endoscopic Cap and Artificial Intelligence Group (endoscopy cap associated with the GI GENIUS ™ IA System)
The purpose of the study is to assess whether the AI characterisation system of the CADDIE device improves the endoscopists accuracy in the optical diagnosis of diminutive colorectal polyps in the bowel during colonoscopy. Participants will either have a colonoscopy with the assistance of the CADDIE device characterisation AI system ("intervention group") or have a colonoscopy in line with routine clinical practice i.e., without the CADDIE device characterisation AI system ("control group"). The randomisation method of this trial will allocate enrolled participants to the "intervention" group and to the "control" group by a technique similar to flipping a coin.
This study is intended to demonstrate the superiority of colorectal polyp detection using computer-assisted colonoscopy compared to conventional colonoscopy.
Patients scheduled for an endoscopic submucosal dissection(ESD) in the colorectum will be randomized to the use of a traction device(consisting of an endoscopic clip with a loop of dental floss secured in the lesion to be removed, another clip will anchor the loop to adjacent bowel wall) or a standard ESD.
This study will be a prospective analysis conducted by Geneoscopy Inc. to evaluate the Colosense test, which is a multi-target stool RNA test for colorectal screening.