View clinical trials related to Colorectal Polyp.
Filter by:The purpose of this study is to develop and validate a deep learning algorithm for the diagnosis of colorectal cancer other colorectal disease by marking and analyzing the characteristics of hyperspectral images based on the pathological results of colonoscopic biopsy, so as to improve the objectiveness and intelligence of early colorectal cancer diagnosis.
This is a prospective, randomized, open-label, non-inferiority, multiple-center trial. Outpatients who are scheduled to undergo colonoscopy and found eligible polyps will be randomized to receive either cold snare endoscopic mucosal resection (CS-EMR) or hot snare endoscopic mucosal resection (HS-EMR). This study aims to compare the efficacy and safety of CS-EMR or HS-EMR for the resection of non-pedunculated colorectal polyps sized 10-19mm.
We have been developing artificial intelligence based polyp histology prediction (AIPHP) method to classify Narrow Band Imaging(NBI) colonoscopy images to predict the hyperplastic or neoplastic histology of polyps. We plan to study colonoscopy polyp samples taken by polypectomy from 1200 patients.The documented NBI still images will be analyzed by the AIPHP method and by the NICE classification parallel.Our aim is to analyze the accuracy of AIPHP and NBI classification based histology predictions and also compare the results of the two methods.
Recent updates of the guidelines on polyp surveillance of the American Society of Gastrointestinal Endoscopy (ASGE) and European Society of Gastrointestinal Endoscopy (ESGE) increasingly focus on size of polyps as an important indicator of malignant transformation to colorectal cancer (CRC). However, the interobserver variability in polyp size assessment between optical diagnosis of endoscopists and pathologists is considerable. This may lead to incorrect surveillance intervals in patients at risk for developing colorectal cancer, which may increase the risk of post-colonoscopy CRC (PCCRC). This study aims to assess the precision of a new laser-based measurement system (AccuMeasure, VTM Technologies Ltd.) for polyps during colonoscopy.
The study involves the planned use of a new microwave-based device during colonoscopy procedures in a small group of patients to assess the preliminary safety of its use and lack of normal clinical practice modification. The device is a final design version, which has been previously tested in several preclinical studies, including: phantom studies, an ex vivo study with human tissues, and an in vivo study with animal model (pig).
Correct endoscopic prediction of the histopathology and differentiation between benign, pre-malignant, and malignant colorectal polyps (optical diagnosis) remains difficult. Artificial intelligence has great potential in image analysis in gastrointestinal endoscopy. Aim of this study is to investigate the real-time diagnostic performance of AI4CRP for the classification of diminutive colorectal polyps, and to compare it with the real-time diagnostic performance of commercially available CADx systems.
Discuss the efficacy and safety of argon plasma coagulation （APC）in comparison with clip closure for preventing colorectal post-procedure bleeding（PPB） after hot snare polypectomy（HSP）; analyze the risk factors and the cost-effectiveness of bleeding prophylaxis strategies with Decision Tree Analytical Method.
The investigators hypothesize that detection of field cancerization in the GI tract could be performed during endoscopy by performing Raman and scattering measurements. Together with the Technical University of Munich (TUM) and the Universidad Carlos III de Madrid (UC3M), the investigators have developed an investigational medical device that integrates probe-based Raman and scattering measurements for endoscopic purposes: the SENSITIVE system. During preclinical ex vivo studies, the investigators have established that measurements of the SENSITIVE system were able to discriminate between non-field cancerized tissue and field cancerized tissue. Considering these results, the investigators aim to assess the safety of in vivo Raman/scattering during endoscopy. Secondly, the investigators to assess the feasibility of this approach measurements to determine field cancerization in the alimentary tract during endoscopy through the SENSITIVE system.
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.