View clinical trials related to Colorectal Polyp.
Filter by:The investigators hypothesize that the clinical implementation of an AI system is an optimal tool to monitor, audit and improve the detection and classification of polyps during colonoscopy. The purpose of this prospective clinical cohort study is to evaluate the performance of the SCALE-EYE virtual scale for measuring polyp size when used during live colonoscopies. The investigators also wish to evaluate CAD-eye for detection and classification of polyp histology. It is hypothesized that CAD-eye and SCALE-EYE can function in real-time practice with high accuracy.
Colonoscopy is the gold standard for colorectal screening. The diagnostic accuracy of colonoscopy highly depends on the quality of inspection of the colon during the procedure. To increase detection new polyp detection systems based on artificial intelligence (AI) have been developed. However, these systems still depend on the ability of the endoscopist to adequately visualize the complete colonic mucosa, especially to detect smaller and more subtle lesions, or lesions hidden behind folds in the colon. With this study we want to combine a device to flatten the folds in the colon combined with an artificial intelligence system to further improve the detection rate of lesions during colonoscopy.
Colonoscopy is an exam which can be responsible for pain and discomfort for the patient. Therefore colonoscopy is performed most of the time under general anaesthesia. Moreover, drug-induced sedation comes with adverse effects especially among fragile patients. Besides, monitoring patients during and after sedation is both logistically demanding and costly. Virtual reality offers immersive and three dimensional experiences that distract the attention and might improve patients comfort. The aim of the study is to investigate the use of virtual reality during colonoscopy versus general anaesthesia.
Colorectal cancer (CRC) is a leading cause of death in the Western world. It can be effectively prevented by removal of pre-malignant polyps (polypectomy) during colonoscopy. Large (≥20mm) non-pedunculated colorectal polyps (LNPCPs) represent 2-3% of colorectal polyps, and require special attention prior to treatment. If submucosal invasion (SMI) is suspected careful decision making is required to exclude features which unacceptably increase the risk of lymph node metastases and render local treatment (endoscopic) non-curative. Such patients require a multi-disciplinary approach and consideration of surgery +/- systemic therapy. Recently the endoscopic imaging characteristics which precisely determine the risk of SMI within colon polyps have been elucidated. This suggests endoscopic imaging may be the ideal investigation to stratify the presence and extent of SMI within LNPCP, particularly as it can be applied in real-time at the time of planned endoscopic treatment. Unfortunately, current classification systems are complex, require extensive training and technology not available in the majority of non-tertiary hospitals. They are therefore underused leading to incorrect decision making and negative patient outcome (e.g piecemeal resection without the chance of endoscopic cure or unnecessary further procedures in referral centres with resultant surgery anyway or surgery for benign disease) A simple clinical support tool was created, based on well-established parameters (i.e., presence of a demarcated area within a polyp, size of the polyp, Paris classification, location within the colon and granularity) to identify OVERT (visible on the surface) and COVERT (hidden) submucosal invasion (SMI) within LNPCPs. Crucially this tool only uses what is reproducible in the majority of endoscopy units in the Western world (i.e. standard magnification, no extra chromic dyes etc). predict SMI within LNPCPs and we translated it into a single web-based clinical support tool that can be used by every endoscopist (expert and non-expert). To evaluate the tool, a survey will be send to participants. The survey consist of a 10-minute educational video where the use of the tool will be explained. Then 20 standardised videos of LNPCPs will be shown. Participants are first asked about their first impression regarding the presence of SMI. Then they are redirected to the web-based tool. After filling the required data from a standardised video (45 seconds to minute, no focus on one particular area of the polyp) the score generated by our tool is copied to the participants computer clipboard and then pasted in the survey so that we could analyse it.
The investigators aim to evaluate the diagnostic accuracy of FIT and the novel panel of four bacterial gene markers collectively named as M3, to detect recurrent advanced adenomas in patients with history of colonic adenomas.
Retrospective study, single blind (patient), allowing a posteriori clinical data collection of 90 patients during their passage to the ambulatory endoscopy circuit, to consider 3 groups and thus to deduce a colonic adenoma detection rate for each arm : - Colonoscopy Only Group - Artificial intelligence only group (IA GI GENIUS ™ alone) - Endoscopic Cap and Artificial Intelligence Group (endoscopy cap associated with the GI GENIUS ™ IA System)
The purpose of the study is to assess whether the AI characterisation system of the CADDIE device improves the endoscopists accuracy in the optical diagnosis of diminutive colorectal polyps in the bowel during colonoscopy. Participants will either have a colonoscopy with the assistance of the CADDIE device characterisation AI system ("intervention group") or have a colonoscopy in line with routine clinical practice i.e., without the CADDIE device characterisation AI system ("control group"). The randomisation method of this trial will allocate enrolled participants to the "intervention" group and to the "control" group by a technique similar to flipping a coin.
Randomised controlled trial comparing cold snare endoscopic mucosal resection (EMR) with cold snare EMR and adjuvant margin STSC in the complete resection of 15-40mm lateral-spreading adenomas
The CELTiC panel is a potential blood-based test for detecting colorectal cancer (CRC) and precursors of CRC. This can be useful for CRC screening, since this requires tests that detect cancer in an early stage to maximize the chances of successful treatment. CELTiC combines four markers that can be detected in blood. These markers are composed of so-called messenger RNA (mRNA) and can be viewed as the instructions of our genes to the cell to make certain proteins. Cancer is the result of mutation in these genes. Thus, the mRNA in cancer patients is, depending on the type of mRNA, often abnormal. In earlier studies, the developers of CELTiC found four mRNA's that are different in patients with CRC compared to healthy individuals. However, CELTiC has not yet been extensively studied in individuals for whom the test is intended: a population undergoing CRC screening. The current study aims to fill this gap. We will assess the ability of CELTiC to detect CRC and precursors of CRC in a population of individuals between 50 and 75 years old in the Netherlands and Italy. This population has already been preselected by having a positive fecal immunochemical test (FIT), a test that is frequently used in CRC screening. This population will undergo a colonoscopy, a procedure where a doctor enters the large bowel through the anus using a flexible camara to assess whether the patient has cancer. Prior to this colonoscopy, we will collect blood samples from the individuals to assess their CELTiC score. After the colonoscopy and the blood analysis, we can assess whether the test adequately detects CRC and precursors of CRC in this population.
Many previous studies had revealed that gastrointestinal microbiome is changed compositionally and ecologically in patients with colorectal cancer comparing with healthy population. These finding provide us with a new sight to take advantage of gut microbiota. The current study aims to explore new potential biomarkers for early screening and prognostic prediction of colorectal cancer and colorectal polyps by analyzing metagenomics and metabolomics of gut microbiota.