View clinical trials related to Adenomatous Polyps.
Filter by:Exploration of a novel rbcDNA liquid biopsy technique for early detection of colorectal cancer is a promising development in the field of disease diagnosis and screening. This technique has the potential to establish an efficient and sensitive system for the early detection of colorectal cancer, which can provide a new perspective for individual health monitoring.
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).
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.
Recently, a CNN-based artificial intelligence (AI) system for polyp characterization has been developed by Fujifilm Co., Tokyo, Japan. It works in conjunction with BLI system. In the present study we prospectively evaluate whether the evaluation of the endoscopist combined with the CAD system output achieve > 90% accuracy in characterization (i.e. as adenomas or non-adenomas) of diminutive rectosigmoid polyps having histopathology as reference standard. Consecutive adult outpatients undergoing elective colonoscopy, in which at least one diminutive (<5 mm) rectosigmoid polyp is detected are included. During endoscopic procedures all polyps identified by the endoscopist are documented for size, location and morphology. All diminutive polyps are characterized by a three sequential steps process: I) endoscopist prediction: the endoscopist evaluates the polyp by using BLI through the BASIC classification; the confidence level (high vs. low) in histology prediction is recorded; II) AI prediction: the AI system is switched on and the output of the automatic evaluation is recorded; this outcome is rated as stable or unstable, depending of the consistency over time of the outcome; III) combined prediction: a final classification is provided by endoscopist in light of the results of the first and of the second step; the confidence level is recorded. All polyps are resected and retrieved in separate jars and sent for pathology assessment. Only polyps characterized with high confidence will be included in the per-polyp analysis; the high-confidence characterization rate will be also calculated; the rate of polyps characterized with a CAD stable outcome will be calculated. Operative characteristics (sensitivity, specificity, positive and negative predictive value and accuracy) in distinguishing adenomatous from non-adenomatous polyps, evaluated with high confidence, will be calculated for each diminutive polyp and for each diminutive rectosigmoid polyp, having histopathology report as reference standard. The post-polypectomy surveillance intervals will be calculated on the basis of polyp histology (reference standard) in all patients according to both USMSTF and ESGE guidelines.
The investigators hypothesize that the clinical implementation of a deep learning AI system is an optimal tool to monitor, audit and improve the detection and classification of polyps and other anatomical landmarks during colonoscopy. The objectives of this study are to generate preliminary data to evaluate the effectiveness of AI-assisted colonoscopy on: a) the rate of detection of adenomas; b) the automatic detection of the anatomical landmarks (i.e., ileocecal valve and appendiceal orifice).
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.
A retrospective and prospective study to determine if the use of ambient lighting during screening colonoscopy is well tolerated and if ambient lighting will help physicians maintain adenoma detection rates while decreasing symptoms of eye strain as the day progresses.
The primary objective is to determine the diagnostic sensitivity and specificity of the newly developed multitarget FIT-DNA Colorectal Cancer (CRC) screening test (ColoClear) for detecting advanced neoplasia (including colorectal cancer and advanced adenomas) in high risk patients, using colonoscopy as the reference method. The secondary objective is to compare the screening performance of the multitarget FIT-DNA test with commercially available FIT (Fecal Immunochemical Test) assay in detecting advanced neoplasia.
The focus of the study is to evaluate impact on Adenomas Per Colonoscopy (APC) with a Computer Aided Detection (CAD) software assisting the gastroenterologist during a colonoscopy procedure.