View clinical trials related to Colonic Polyps.
Filter by:Colonoscopic removal of adenomatous polyps reduce both the incidence and mortality of colorectal cancer (CRC). The common clinical management of colorectal polyp detected during colonoscopy is to remove them and send for histopathology to determine the subsequent surveillance interval. More than 80% of polyps detected during screening or surveillance colonoscopy are diminutive (≤5mm). As the chance of diminutive polyps to harbor cancer or advanced neoplasia is low, leave-in-situ and resect-and-discard strategies using optical diagnosis are recommended for non-neoplastic polyps by the American Society for Gastrointestinal Endoscopy (ASGE) and the European Society for Gastrointestinal Endoscopy (ESGE) so as to reduce the financial burden of polypectomy and histopathology. The societies proposed leave-in-situ strategy if optical diagnosis can achieve a negative predictive value (NPV) of >90% for rectosigmoid polyp and resect-and-discard if an agreement of more than 90% concordance with histopathology-based post-polypectomy surveillance interval can be achieved. However, optical diagnosis is operator dependent and most endoscopists are reluctant to adopt this strategy in routine practice because of the need of strict training and auditing and fear of incorrect diagnosis. In the past decade, with the exponential increase in computational power, reduced cost of data storage, improved algorithmic sophistication, and increased availability of electronic health data, artificial intelligence (AI) assisted technologies were widely adopted in various healthcare settings to improve clinical outcomes, especially the quality of colonoscopy in the area of gastroenterology. Real time use of computer-aided diagnosis (CADx) for adenoma using AI systems were developed and proven to be useful to help endoscopists to distinguish neoplastic polyps from non-adenomatous polyps. However, these studies only examined diminutive polyp but not polyp of larger size (>5mm). They were conducted with small sample size of less than few hundred subjects and the study settings were open-label and non-randomized. The investigators aim to conduct a large scale randomized controlled trial to evaluate the performance of colorectal polyp characterization of all size polyps by real-time CADx using AI system against conventional colonoscopy with optical diagnosis.
Colonoscopy is the gold standard modality for the detection of colonic polyp. However, miss polyp occurs especially in right sided colon. Artificial intelligence (AI) is one of the modality to improve polyp detection but the benefit of AI in operators with different endoscopic experience is still limited. This study aimed to evaluate the efficacy of AI in the detection of right sided colonic polyp in operators with different endoscopic experience by using double insertion of right side colon, back-to-back basis.
This is a randomized controlled multicenter clinical trial of computer-aided detection (CADe) system for the adjuvant diagnosis of intestinal polyps/adenomas ever conducted in a Chinese population. In addition, this study will evaluate the effect of CADe system on adenoma detection of endoscopists under fatigue.
A randomized controlled crossover study to determine if narrow band imaging or white light endoscopy is superior in detecting right colonic polyps in average risk subjects undergoing screening colonoscopy
Cold polypectomy has the advantages of simple operation, less time-consuming and fewer complications. Guidelines have recommended cold snare polypectomy (CSP) to resect small polyps sized <9 mm. CSP was designed to improve the complete resection rate and reduce adverse events. Investigators hypothesize that CSP is better than conventional hot snare endoscopic mucosal resection (HS-EMR) in the presence of injured submucosal arteries detected in the submucosal layer for 10-19 mm nonpedunculated colorectal polyps, resulting in lower delayed bleeding after CSP of 10-19 mm nonpedunculated colorectal polyps.
The goal of this trial is to determine whether use of a Computer Assisted Detection (CADe) programme leads to an increase in ADR for either units or individual colonoscopists, independent of setting or expertise
The objective of this study is to collect colonoscopy data for use in the development and testing of artificial intelligence (AI) devices for colonoscopies.
CCIS is a novel score, created specifically to evaluate the completeness of caecal visualized. It can be applied to a single or multiple images. To create the CCIS, the caecum was divided into eight parts: the appendiceal orifice (AO), the tri-radiate fold part 1 (TF-1), 2 (TF-2), 3 (TF-3) and four outer quadrants (OQ 1-4). The ileo-caecal valve (ICV) is a reference point but is not part of the score. The quadrant adjacent to the ICV is labelled OQ1. The three other quadrants are labelled clockwise from this quadrant. The tri-radiate folds are also labelled clockwise with TF1 representing the triangle side that is majority-contained within OQ1. TF2 and TF3 are then labelled clockwise from TF1.
It is estimated that about 20% of colonoscopies have inadequate preparation. (5) This is associated with lengthy procedures and less detection of adenomas, reduces the screening intervals, and increases the costs and risks of complications. Several strategies have been proposed to improve the quality of bowel preparation. Mobile healthcare Apps have been developed to increase adherence to bowel preparation agents, improving the quality of bowel preparation. However, adherence to mobile healthcare Apps is also a quality criterion and a pending problem to solve with this new technology. GastroBot is a new technology based on artificial intelligence that allows, through a software bot, to carry out a personalized follow-up of the patient's bowel cleansing, advising the patient to overcome contingencies that arise with the preparation, which in other circumstances could lead to the failure of it. The primary aim of this study is to determine the improvement in bowel preparation after GastroBot assistance compared with the traditional explanation. As a secondary aim, this study also pursues to determine adenoma and polyp detection rates (ADR and PDR, respectively), bowel preparation agents' tolerance, and GastroBot functionality.
The present trial is aimed at evaluating whether in individuals scheduled for colonoscopy in the framework of a structured FIT (Fecal Immunochemical stool test)-based colorectal cancer screening program, the combination of an AI (artificial intelligence) system (CADEYE) with a mucosal exposure device (G-EYE 760R endoscope) increases the identification of subjects at high risk to develop colorectal cancer (according to recent ESGE-European Society of Gastrointestinal Endoscopy guidelines subjects are labelled as "high-risk" if harboring at least 1 adenoma ≥ 10 mm or with high grade dysplasia, or ≥ 5 adenomas, or any serrated polyp ≥ 10 mm or with dysplasia) when compared to colonoscopy performed with the support of AI only. Individuals fulfilling inclusion criteria are randomized (1:1) to two different arms (Control arm and Interventional arm, see below). Randomization is based on a computer-generated randomized block sequence, stratified according to age (50-61 vs. 62-74) and gender (male vs. female); size of the blocks (10 individuals) is not communicated to the investigator. Allocation is concealed and kept in a sealed envelope, which is opened just before starting colonoscopy. Individuals randomized in the Intervention arm receive colonoscopy examination with G-EYE 760R colonoscopes; once the cecum is reached the balloon is inflated, and the endoscope is withdrawn with the inflated balloon; the colonoscopy is performed with the support of the CADEYE system for polyp detection in both insertion and withdrawal phase; all polyps identified are removed and sent for histopathology examination. Individuals randomized in the Control arm (CADEYE only) receive colonoscopy with G-EYE 760R colonoscope but the balloon remains deflated for the entire procedure; the colonoscopy is performed with the support of the CADEYE system for polyp detection in both insertion and withdrawal phase; all polyps identified are removed and sent for histopathology examination. The main outcome measure is the rate of "high risk" individuals across the two study arms.