View clinical trials related to Colonic Polyps.
Filter by: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).
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.
The Italian screening program invites the resident population aged 50-74 for Fecal Immunochemical Test (FIT) every 2 years. Subjects who test positive are referred for colonoscopy. Maximizing adenoma detection during colonoscopy is of paramount importance in the framework of an organized screening program, in which colonoscopy represent the key examination. Initial studies consistently show that Artificial iIntelligence-based systems support the endoscopist in evaluating colonoscopy images potentially increasing the identification of colonic polyps. However, the studies on AI and polyp detection performed so far are mostly focused on technical issues, are based on still images analysis or recorded video segments and includes patients with different indications for colonoscopy. At the best of our knowledge, data on the impact on AI system in adenoma detection in a FIT-based screening program are lacking. The present prospective randomized controlled trial is aimed at evaluating whether the use of an AI system increases the ADR (per patient analysis) and/or the mean number of adenomas per colonoscopy in FIT-positive subjects undergoing screening colonoscopy. Therefore Patients fulfilling the inclusion criteria are randomized (1:1) in two arms: A) patients receive standard colonoscopy (with high definition-HD endoscopes) with white light (WL) in both insertion and withdrawal phase; all polyps identified are removed and sent for histopathology examination; B) patients receive colonoscopy examinations (with HD endoscopes) equipped with an AI system (in both insertion and withdrawal phase); all polyps identified are removed and sent for histopathology examination. In the present study histopathology represents the reference standard.
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.
Complete polypectomy is one of the major factors for effectiveness of colonoscopy to prevent colon cancer. Given the prevalence of the 4-6 mm polyp, and the concern about interval cancers at polypectomy sites, there is a clear and significant need to determine which technique(s) are most appropriate for clinical practice. This study was to compare the three commonly used polypectomy techniques in terms of efficacy and efficiency.
The mains complications in colo-rectal dissection are the pain, the delayed bleeding and the perforation and represent around 10%. Currently, the procedure is realized during a hospitalization with not real recommendation about the time of this. There is currently no score established for the colo-rectal endoscopic submucosal dissection. - To develop clinical or mixed prognostic score after endoscopic subcostal dissection for colorectal lesions in Nancy's hospital. - Allow to obtain an estimation of number of patients required for a larger study.
The focus of the study is to evaluate impact of submucosal injection of EverLift in achieving complete resection during polypectomy of polyps 4-9mm during colonoscopy.
The focus of the study is to evaluate impact of cold forcep and cold snare in achieving complete resection during polypectomy of polyps <=3mm during colonoscopy.
The purpose of this study is to assess whether computer aided technology (CAD) can help in the diagnosis of polyps found the bowel compared with visual inspection alone and therefore whether it is beneficial in helping clinicians to decide whether to remove a polyp or not. Presently, most endoscopists remove all polyps found and send them to the laboratory for testing. The number of colonoscopies is increasing, meaning that more polyps are detected and removed. This comes at a significant cost to the health service and increases the time taken to complete a colonoscopy.
The identification of risk factors of colorectal/gastric polyp is more helpful for preventing colorectal cancer. And modifiable factors (such as high-fat diet, abnormal blood lipid, smoking, lack of exercise, obesity), and unmodifiable factors (including age, gender, race, familial adenomas, genetic)) can affect the risk of polyps. Thus early studying risk factors are the key to improving prognosis. what's more, early detection and timely treatment have important clinical significance for preventing and reducing the occurrence of gastrointestinal cancer.