View clinical trials related to Colonic Polyp.
Filter by:The global burden of colorectal cancer (CRC) incidence among young age groups is rising and overwhelming. This new trend of young-onset CRC incidence is evident in western countries. Unfortunately, Asian countries have shown the same epidemic shift in the past few years. As a consequence, this situation might necessitate revisiting the current screening program in this region. Saudi Arabia has a two-fold increase in CRC incidence among young age groups in the last 18 years (9.6/100000 for male versus 9.3/100000 for female). This rising incidence ascribed to the lack of a screening program and suggested lowering CRC screening to 40. The low awareness about risk factors, signs, and symptoms of the disease causes late presentation of CRC cases. Therefore, most presenting cases are associated with a poor prognosis and short survival. Educational and screening programs are, by no means, considered valuable and essential as CRC tends to affect younger age groups.
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).
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.
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.
This controlled-randomized trial compares the artificial intelligence Genius® system assisted (Genius+) to standard (Genius-) colonoscopy. The aim of this study was to evaluate the impact of Genius® system on ADR in routine colonoscopy. The secondary aims will be the impact of Genius® system on polyp detection rate (PDR), serrated polyp detection rate (SPDR), advanced neoplasia detection rate (ANDR), mean number of polyps (MNP), polyp type and localization, and operator type (according to basal ADR).
The objective of this study is to compare the polyp detection rate (PDR) of endoscopists unaware of a commercially available artificial intelligence (AI) device for polyp detection during colonoscopy and the PDR of endoscopists with the aid of such a device. Moreover, an extensive characterization of the performance of this device will be done.
A prospective validation of real time deep learning artificial intelligence model for detection of missed colonic polyps
This is a single centre randomised controlled study comparing underwater clip closure versus conventional gas insufflation clip closure of post-resection defect in patients undergoing colonic endoscopic resection. The investigators hypothesize that underwater clip closure would be faster than conventional closure under gas insufflation.