View clinical trials related to Colonic Polyp.
Filter by:This study is aiming to enroll 90 patients with genetically confirmed Lynch Syndrome (LS) from Geisinger's High Risk Colorectal Cancer Clinic (HRC). Upon enrollment in the study, a Cologuard test will be ordered and the results will be blinded until data analysis. Patients enrolled in the study will also undergo a colonoscopy as part of their routine HRC visit.
Computer aided detection (CADe) algorithms have been developed to overcome human errors and assist endoscopists in detecting more polyps during colonoscopy. The aim of this study was to investigate the accuracy of the novel Pentax Discovery CADe system (Discovery-AI) against pre-recorded videos of colon polyps of various size, shape and pathology while using videos of normal colon segments as controls from two different institutes.
Comparison between a 1L of polyethylene glycol+ascorbic acid as a split dose and oral sulfate solution bowel preparation for colonoscopy study design: prospective, randomized, parallel, multi-center trial in 3 hospitals in Korea ( Seoul National University hospital, Seoul National University Bundang hospital, Seoul Metropolitan Government-Seoul National University ) patient inclusion criteria - aged 20-75 adults (out-clinic patients) scheduled for colonoscopy for any indication within the normal process of care
COLO-DETECT is a clinical trial to evaluate whether an Artificial Intelligence device ("GI Genius", manufactured by Medtronic) can identify more polyps (pre-cancerous growths of the bowel lining) during colonoscopy (large bowel camera test) than during colonoscopy without it.
This is a prospective feasibility study. The aim of this work is to assess the acceptability and feasibility of optical diagnosis-led care in bowel cancer screening patients undergoing colonoscopy. This study will determine whether bowel cancer screening colonoscopists are able to consistently record and diagnose diminutive adenomas suitable for a resect and discard strategy allowing assignment of surveillance intervals according to Preservation and Incorporation of Valuable Endoscopic Innovations (PIVI) criteria. A practical quality assurance program around optical diagnosis will be introduced. The use of a CAD polyp-detection system will also be evaluated (AI-DETECT).
PURPOSE OF PROTOCOL Objective: To evaluate if the speedometer based on the real-time image analysis can help endoscopists increase their withdrawal time, which is defined as time spent examining the colon during withdrawal of the colonoscope, during colonoscopy. Hypothesis: The trial hypothesis is that use of the speedometer during colonoscopy will increase the average withdrawal time, which is defined as time spent examining the colon during withdrawal of the colonoscope, by 1.6 minutes, possibly increasing the performance of the participating endoscopists. Our objective is to clarify the clinical benefits of this digital tool in colonoscopy. Endpoint: Withdrawal time difference between colonoscopies done without the speedometer (control period) and colonoscopies done with the speedometer (intervention period).
Colonoscopy is the gold standard for detection and removal of precancerous lesions, and has been amply shown to reduce mortality. However, the miss rate for polyps during colonoscopies is 22-28%, while 20-24% of the missed lesions are histologically confirmed precancerous adenomas. To address this shortcoming, the investigators propose a new polyp detection system based on deep learning, which can alert the operator in real-time to the presence and location of polyps during a colonoscopy. The investigators dub the system DEEP: (DEEP) DEtection of Elusive Polyps. The DEEP system was trained on 3,611 hours of colonoscopy videos derived from two sources, and was validated on a set comprising 1,393 hours of video, coming from a third, unrelated source. For the validation set, the ground truth labelling was provided by offline gastroenterologist annotators, who were able to watch the video in slow-motion and pause/rewind as required; two or three specialist annotators examined each video. This is a prospective, non-blinded, non-randomized pilot study of patients undergoing elective screening and surveillance colonoscopies using DEEP. The aim of the study is to: Assess the: 1. Number of additional polyps detected by the DEEP system in real time colonoscopy. 2. Safety by prospective assessment of the rate of adverse events during the study period attributed or not to the use of the DEEP system. 3. Stability of the DEEP system by measuring the rate of false positives (False Alarms) per colonoscopies 4 And to examine its feasibility and usefulness of in clinical practice by assessing the colonoscopist user experience while using the DEEP system in a 5 point scale.
Deep learning technology has an increasing role in medical image applications and, recently, an artificial intelligence device has been developed and commercialized by Medtronic for identification of polyps during colonoscopy (GI-GENIUS). This kind of computer-aided detection (CADe) devices have demonstrated its ability for improving polyp detection rate (PDR) and the adenoma detection rate (ADR). However, this increase in PDR and ADR is mainly made at the expense of small polyps and non advanced adenomas. Colonoscopies after a positive fecal immunochemical test (FIT) could be the scenario with a higher prevalence of advanced lesions which could be the ideal situation for demonstrating if these CADe systems are able also to increase the detection of advanced lesions and which kind of advanced lesions are these systems able to detect. The CADILLAC study will randomize individuals within the population-based Spanish colorectal cancer screening program to receive a colonoscopy where the endoscopist is assisted by the GI-GENIUS device or to receive a standard colonoscopy. If our results are positive, that could suppose a big step forward for CADe devices, in terms of definitive demonstration of being of help for efectively identify also advanced lesions.
Introduction It has been shown that some quality indicators in endoscopy can be improved through educational interventions. There are marked differences in the proportion of incomplete polypectomies among endoscopists. The effectiveness of measures to improve it has not been evaluated. Objective The main objective is to evaluate whether a training intervention or the notification of the individual proportion of incomplete polypectomies (those in which post-polypectomy biopsies of the resection margin show tissue other than normal mucosa) can improve this proportion. As secondary objectives, we will compare the proportion of fragmented polypectomies and adverse events. We will evaluate the factors associated with incomplete excision or failed cold polypectomy, as well as the individual evolution of the participants. Methods Non-pharmacological clinical trial involving endoscopists with> 1 year of experience and patients scheduled for colonoscopy. After each polypectomy, 2 additional biopsies will be taken and evaluated centrally by a blind pathologist. In a first phase, the basal rate of the participants will be evaluated. After it, the endoscopists will receive a course on endoscopic polypectomy and the other their rate of complete resection. The number of polyps required will vary depending on the number of endoscopists The primary objective will be compared using logistic regression models based on generalized estimating equations (GEE), taking into account the within-subject correlation.
This study will evaluate the safety and effectiveness of the CapsoCam Colon (CV-3) endoscope system for the detection of colonic polyps.