View clinical trials related to Colorectal Polyp.
Filter by:Investigators developed an online educational module (ESTIMATE) to teach Gastroenterology (GI) trainees how to estimate polyp size using a snare. Key components include video instruction and real-time feedback incorporated over a 40-item polyp size assessment test. Trainees from GI fellowship programs will be randomized to one of four groups: control (no video, no feedback), video-only, feedback-only, and video + feedback. Participants will classify polyps into one of three size categories:- diminutive (1-5 mm), small (6-9 mm), and large (≥10 mm). Primary outcome is accuracy of polyp size classification [diminutive (1-5 mm), small (6-9 mm), and large (≥10 mm)]. Secondary outcomes include accuracy of exact polyp size (in mm), cumulative accuracy (to plot learning curves), confidence level of polyp size classification, and directionality of inaccuracy (polyp size overestimation vs underestimation).
Background: Colonoscopy is accepted to be the gold standard for screening of colorectal cancer (CRC). Most CRCs develop from adenomatous polyps, with colonoscopy accepted to be the gold standard for screening of CRC. An endoscopist's ability to detect polyps is assessed in the form of an Adenoma Detection Rate (ADR). Each 1.0% increase in ADR is associated with a 3.0% decrease in the risk of the patient developing an interval CRC. There remains a wide variation in endoscopist ADR. More recently, the use of artificial intelligence (AI) and computer aided diagnosis in endoscopy has been gaining increasing attention for its role in automated lesion detection and characterisation. AI can potentially improve ADR, but previous AI related work has largely focused on retrospectively assessing still endoscopic images and selected video sequences which may be subject to bias and lack clinical utility. There are only limited clinical studies evaluating the effect of AI in improving ADR. The CADDIE device uses convolutional neural networks developed for computer assisted detection and computer assisted diagnosis of polyps. Primary objective: To determine whether the CADDIE artificial intelligence system improves endoscopic detection of adenomas during colonoscopy. Primary endpoint: The difference in adenoma detection rate (ADR) between the intervention (supported with the CADDIE system) and non-intervention arm Study design: Multi-Centre, open-label, randomised, prospective trial to assess efficacy and safety of the CADDIE artificial intelligence system for improving endoscopic detection of colonic polyps in real-time.