View clinical trials related to Colorectal Polyp.
Filter by: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 prospective case-control study aimed to analyze and summarize the high-risk factors and susceptible genes of patients with colorectal polyps. According to these high-risk factors, the investigators developed and validated a prediction model for colorectal polyps to identify high-risk individuals, in order to provide clinical basis for the etiology research and the establishment of effective preventive measures.
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.
This study is a prospective, single-arm, open-label, multi-center, feasibility study to evaluate the safety and efficacy of ColubrisMX ELS System in patients undergoing transanal endoluminal procedures, specifically colorectal Endoscopic Submucosal Dissection.
Bowel cancer is the third most common cancer in the UK. It develops through smaller growths in the bowel called polyps. Early recognition and removal of these polyps result in prevention of developing bowel cancer in an individual. However, not all polyps will lead to cancer, certain polyps are just growths of normal tissue and can be left in the bowel. We therefore need to know which polyps to remove and which ones to leave. One way of doing this is to have a better look at these polyps. This can be done by new technologies. One of them is called Blue Light Imaging (BLI). This is a new light source at the end of the camera which is activated by the push of a button. It will help us in looking at these polyps more closely. This helps us decide which polyps to remove and which ones are safe to leave as there is always a small risk in removing a polyp. It would also give us a better idea as to when to repeat the camera test if necessary (endoscopic surveillance). By reducing the number of polyps resected and sent to the pathology labs for diagnosis, the work load on the pathology department is also reduced and in the process, providing cost savings to the Trust, The study aims to see if using Blue Light during endoscopy helps us to identify and characterize small polyps better
Rationale: Diminutive colorectal polyps (1-5mm in size) have a high prevalence and very low risk of harbouring cancer. Current practice is to send all these polyps for histopathological assessment by the pathologist. If an endoscopist would be able to correctly predict the histology of these diminutive polyps during colonoscopy, histopathological examination could be omitted and practise could become more time- and cost-effective. Studies have shown that prediction of histology by the endoscopist remains dependent on training and experience and varies greatly between endoscopists, even after systematic training. Computer aided diagnosis (CAD) based on convolutional neural networks (CNN) may facilitate endoscopists in diminutive polyp differentiation. Up to date, studies comparing the diagnostic performance of CAD-CNN to a group of endoscopists performing optical diagnosis during real-time colonoscopy are lacking. Objective: To develop a CAD-CNN system that is able to differentiate diminutive polyps during colonoscopy with high accuracy and to compare the performance of this system to a group of endoscopist performing optical diagnosis, with the histopathology as the gold standard. Study design: Multicentre, prospective, observational trial. Study population: Consecutive patients who undergo screening colonoscopy (phase 2) Main study parameters/endpoints: The accuracy of optical diagnosis of diminutive colorectal polyps (1-5mm) by CAD-CNN system compared with the accuracy of the endoscopists. Histopathology is used as the gold standard.
Our group, prior to the present study, developed a handcrafted predictive model based on the extraction of surface patterns (textons) with a diagnostic accuracy of over 90%24. This method was validated in a small dataset containing only high-quality images. Artificial intelligence is expected to improve the accuracy of colorectal polyp optical diagnosis. We propose a hybrid approach combining a Deep learning (DL) system with polyp features indicated by clinicians (HybridAI). A pilot in vivo experiment will carried out.
The main aim of this study is to determine whether the assessment of the invasive pattern based on NBI with dual focus/magnification or BLI with magnification ± chromoendoscopy (NBI+CE) for predicting deep invasion is significantly more accurate than the assessment based on white light endoscopy (WLE), carried out by trained endoscopists.
This study has three main purposes：screening: the first purpose is to evaluate the diagnostic value of combintion of the life risk factors and immunochemical fecal occult blood test (FIT) on detection of colorectal neoplasia in Chinese population; resection: the second objective is to investigate the complete resection rate of colorectal adenoma and risk factors of incomplete resection in China; identification and classification: the third objective is to initially establish an artificial intelegence-assissted recognition and classification system of polyp based on deep learning.
The study aims to compare the results between colonoscopies with two different attachments on the distal end of the colonoscope.