View clinical trials related to Colonic Dysplasia.
Filter by:Colorectal cancer (CRC) is a leading cause of cancer-related morbidity and mortality worldwide, with rates of CRC predicted to increase. Colonoscopy is currently the gold standard of screening for CRC. Artificial intelligence (AI) is seen as a solution to bridge this gap in adenoma detection, which is a quality indicator in colonoscopy. AI systems utilize deep neural networks to enable computer-aided detection (CADe) and computer-aided classification (CADx). CADe is concerned with the detection of polyps during colonoscopy, which in turn is postulated to help decrease the adenoma miss-rate. In contrast, CADx deals with the interpretation of polyp appearance during colonoscopy to determine the predicted histology. Prediction of polyp histology is crucial in helping Clinicians decide on a "resect and discard" or "diagnose and leave strategy". It is also useful for the Clinician to be aware of the predicted histology of a colorectal polyp in determining the appropriate method of resection in terms of safety and efficacy. While CADe has been studied extensively in randomized controlled trials, there is a lack of prospective data validating the use of CADx in a clinical setting to predict polyp histology. The investigators plan to conduct a prospective, multi-centre clinical trial to validate the accuracy of CADx support for prediction of polyp histology in real-time colonoscopy.
Small growths detected in the colon (polyps) during a colonoscopy may or may not have the potential to develop into cancer. However, since visual inspection alone cannot separate all potentially harmful polyps from harmless ones, the standard approach is to remove them all for histological lab examination, exposing patients to risk of injury and putting a significant demand on hospital resources. An accurate method of determining polyp type during endoscopy would enable the clinician to only remove potentially harmful polyps. A new endoscopic optical imaging probe (OPTIC), which analyses how light interacts with tissue, is proposed to do this. The probe is contained within a normal endoscope and uses white light and blue/violet laser light to illuminate the tissue. The reflected and fluorescent light emitted, along with normal colour pictures of the polyp surface, are measured and recorded to quantify specific characteristics of each type. Optical measurements of polyps detected in endoscopy clinics at Imperial College Healthcare NHS Trust will be analysed to determine if the signal can be used to differentiate different polyp types.