View clinical trials related to Colonic Polyps.
Filter by:The aim of this study was to assess the performance of AI system in polypectomy
Patients who met the criteria for removal of 10-19mm colorectal polyps using cold snare or hot snare were included in the study, signed by endoscopic treatment written informed consent for surgery, patients with detailed tracking and record the basic information and information related to the operation, postoperative lack of region and edge endoscopic observation carefully no residue, additional excision may be took if necessary，after resection specimen inspection, and in 6 months review colonoscopy, assess whether there is residual or recurrence of polyps.Main outcome: technical success rate (no other auxiliary resection), complete resection rate, secondary outcome: intraoperative and postoperative complications, polypectomy time and related costs, influential factors of incomplete resection.Research significance: The effectiveness, safety and cost-effectiveness of cold and hot snare resection of 10-19mm colorectal polyps were compared, and the influencing factors of incomplete polyps resection were analyzed, so as to provide evidence for the decision on the best method of medium-size polyps resection.
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.
Even if colonoscopy is considered the reference standard for the detection of colonic neoplasia, polyps are still missed. The risk of early post-colonoscopy cancer appeared to be independently predicted by a relatively low polyp/adenoma detection rate. When considering the very high prevalence of advanced neoplasia in the FIT-positive enriched population, the risk of post-colonoscopy interval cancer due to a suboptimal quality of colonoscopy may be substantial. Available evidence justifies therefore the implementation of efforts aimed at improving adenoma detection rate, based on retraining interventions and on the adoption of innovative technologies, designed to enhance the accuracy of the endoscopic examination. Artificial intelligence seems to improve the quality of medical diagnosis and treatment. In the field of gastrointestinal endoscopy, two potential roles of AI in colonoscopy have been examined so far: automated polyp detection (CADe) and automated polyp histology characterization (CADx). CADe can minimize the probability of missing a polyp during colonoscopy, thereby improving the adenoma detection rate (ADR) and potentially decreasing the incidence of interval cancer. GI Genius is the AI software that will be used in the present trial and is intended to be used as an adjunct to colonic endoscopy procedures to help endoscopists to detect in real time mucosal lesions (such as polyps and adenomas, including those with flat (non-polypoid) morphology) during standard screening and surveillance endoscopic mucosal evaluations. It is not intended to replace histopathological sampling as a means of diagnosis. The objective of this study was to compare the diagnostic yield obtained by using CADe colonoscopy to the yield obtained by the standard colonoscopy (SC).
This controlled-randomized trial compares the artificial intelligence Genius® system assisted (Genius+) to standard (Genius-) colonoscopy. The aim of this study was to evaluate the impact of Genius® system on ADR in routine colonoscopy. The secondary aims will be the impact of Genius® system on polyp detection rate (PDR), serrated polyp detection rate (SPDR), advanced neoplasia detection rate (ANDR), mean number of polyps (MNP), polyp type and localization, and operator type (according to basal ADR).
Interventional prospective multicenter study: Polyp detection by an automated endoscopic tool as second observer during routine diagnostic colonoscopy
Colonoscopy is currently accepted as the gold standard in screening, surveillance and prevention for colorectal cancer (CRC), and therefore, its quality is a major priority. The quality of colonoscopy is greatly dependent on the quality of the bowel preparation, which can be limited by stool, foam, bubbles and other debris. In fact, colonic bubbles are described in 30 to 40% of colonoscopies, possibly undermining the quality of the exam, impairing the endoscopists view, demanding the further use of water or simethicone and eventually increasing fatigue and costs, while diminishing diagnostic accuracy. Although previous attempts, to date no endoscopic scale is validated regarding the presence of bubbles and most widely accepted and already validated scales do not include the presence or absence of bubbles in their definition, leading to the use of different home-made scales in randomized trials and impairing any solid meta-analysis conclusion. As so, the goal of this study is to develop and validate a new colonic bubble score (Colon Endoscopic Bubble Scale - CEBuS).
In this observational pilot study, we assess the diagnostic performance of an artificial intelligence sytem for automated detection of colorectal polyps.
In the past decade, the demand for colonoscopy procedures has increased significantly since the introduction of population-based colorectal cancer (CRC) screening in many western countries. Post-polypectomy surveillance will increase the number of colonoscopy procedures conducted each year even further. The invasive nature of colonoscopy and the associated health-care costs warrant the development of a new non-invasive test to reduce the number of unnecessary colonoscopies. These days, many countries use a non-invasive fecal test for CRC screening which is easy to perform at home, but test characteristics such as sensitivity and specificity are suboptimal. Multiple studies have already shown that volatile organic compound (VOC) analysis has a high diagnostic accuracy for CRC and Advanced Adenomas. An additional VOC analysis, for example through breath testing, in patients with a positive fecal immunochemical test (FIT) may reduce the number of unnecessary colonoscopies. The aim of this study is to validate the diagnostic accuracy of the AeonoseTM to distinguish patients with CRC from healthy controls, and to assess reproducibility of test results.