View clinical trials related to Polyps.
Filter by:Colorectal cancer is the second most common cancer in Canada. Colonoscopy and removal of precancerous polyps (polypectomy) reduces the incidence and mortality associated with colorectal cancer. However, polypectomy is associated with adverse events. Post-polypectomy bleeding has a significant impact on the life of the patient as it can require hospitalization, transfusions, repeat colonoscopy and rarely death. It is also a substantial cost to the health care system. There currently is no standard of care to prevent bleeding after polypectomy. Tranexamic acid reduces fibrinolysis by slowing down the conversion of plasminogen to plasmin which may prevent bleeding. Although this medication is used extensively for other purposes, it has not been studied before to prevent post-polypectomy bleeding. This pilot study will examine factors involved in the feasibility of conducting a large-scale randomized controlled trial (RCT). This pilot study will include 25 consecutive patients who are treated with tranexamic acid after endoscopic mucosal resection (EMR) of large non-pedunculated colorectal polyps (LNPCP's) to prevent PPDB.
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.
This study is an open label, unblinded, non-randomized interventional study, comparing the investigational artificial intelligence tool with the current "gold standard": Data acquisition will be obtained during one scheduled colonoscopic procedure by a trained endoscopist. During insertion, no action will be taken, colonoscopy is performed following the standard of care. Once withdrawal is started, a second observer (not a trained endoscopist but person trained in polyp recognition) will start the bedside Artificial intelligence (AI) tool, connected to the endoscope's tower, for detection. This second observer is trained in assessing endoscopic images to define the AI tool's outcome. Due to the second observer watching the separate AI screen, the endoscopist is blinded of the AI outcome. When a detection is made by the AI system that is not recognized by the endoscopist, the endoscopist will be asked to relocate that same detection and to reassess the lesion and the possible need of therapeutic action. All detections are separately counted and categorized by the second observer. All polyp detections will be removed following standard of care for histological assessment. The entire colonoscopic procedure is recorded via a separate linked video-recorder.
Background We are developing artificial intelligence based polyp histology prediction (AIPHP) method to automatically classify Narrow Band Imaging (NBI) magnifying colonoscopy images to predict the non-neoplastic or neoplastic histology of polyps. Aim Our aim was to analyse the accuracy of AIPHP and NICE classification based histology predictions and also to compare the results of the two methods. Methods We examined colorectal polyps obtained from colonoscopy patients who had polypectomy or endoscopic mucosectomy. Polyps detected by white light colonoscopy were observed then by using NBI at the optical maximum magnificent (60x). The obtained and stored NBI magnifying images were analysed by NICE classification and by AIPHP method parallelly. Pathology examinations were performed blinded to the NICE and AIPHP diagnosis, as well. Our AIPHP software is based on a machine learning method. This program measures five geometrical and colour features on the endoscopic image.
Interventional prospective multicenter study: Polyp detection by an automated endoscopic tool as second observer during routine diagnostic colonoscopy
Accurate optical diagnosis of colorectal polyps could allow a "resect and discard" strategy based on the results of the optical biopsy. Even though intensive training for optical diagnosis, there is still wide variability in individual endoscopists to meet the PIVI thresholds. The investigators with experience of prior optical diagnosis training perform new education and drill to apply proper high confidence according to their decision time. After the education program, the investigators prospectively evaluate real-time optical biopsy analysis of polyps in 8 academic gastroenterologists.
Primary, this study aims to develop and validate a computer-aided diagnosis (CADx) system for the characterization of colorectal polyps. Second, this study evaluates the effect of using a clinical classification model Blue Light Imaging Adenoma Serrated International (BASIC) on the diagnostic accuracy of the optical diagnosis of colorectal polyps compared to intuitive optical diagnosis for both expert endoscopists and novices.