Active clinical trials for "Colonic Polyps"

Background: Removal of adenomatous polyps during colonoscopy is associated with long-term prevention of colorectal cancer-related deaths. Recently, there have been much interest in the use of artificial intelligence (AI) platforms to augment the routine endoscopic assessment of the colon to enhance adenoma detection rate (ADR). To date, computer assisted detection of polyps (CADe) have been shown to be safe, with a significant increase in ADR, without any concomitant increase in post-procedural complications. Aims: The investigators aim to evaluate the use of GI GeniusTM Intelligent Endoscopy Module in a multi-ethnic Asian population (Singapore) to increase in ADR and adenoma detected per colonoscopy (ADPC)to justify its effectiveness as an adjunct in polyp detection and training for colonoscopy. Methods: This study will be a single-institution cohort study, conducted over a 2-year period. Sengkang General Hospital (SKH) does an estimated 12,500 colonoscopies per year, with an average of 1,040 colonoscopies performed every month. Thus, given the case volume, the investigators expect to detect differences in ADR amongst endoscopists if any during this study period. As part of the subgroup analysis, the investigators also aim to compare the ADR rates of trainee endoscopists with and without the GI GeniusTM Intelligent Endoscopy Module to ascertain its utility as an education tool/training adjunct

The present trial is aimed at evaluating whether in individuals scheduled for colonoscopy in the framework of a structured FIT (Fecal Immunochemical stool test)-based colorectal cancer screening program, the combination of an AI (artificial intelligence) system (CADEYE) with a mucosal exposure device (G-EYE 760R endoscope) increases the identification of subjects at high risk to develop colorectal cancer (according to recent ESGE-European Society of Gastrointestinal Endoscopy guidelines subjects are labelled as "high-risk" if harboring at least 1 adenoma ≥ 10 mm or with high grade dysplasia, or ≥ 5 adenomas, or any serrated polyp ≥ 10 mm or with dysplasia) when compared to colonoscopy performed with the support of AI only. Individuals fulfilling inclusion criteria are randomized (1:1) to two different arms (Control arm and Interventional arm, see below). Randomization is based on a computer-generated randomized block sequence, stratified according to age (50-61 vs. 62-74) and gender (male vs. female); size of the blocks (10 individuals) is not communicated to the investigator. Allocation is concealed and kept in a sealed envelope, which is opened just before starting colonoscopy. Individuals randomized in the Intervention arm receive colonoscopy examination with G-EYE 760R colonoscopes; once the cecum is reached the balloon is inflated, and the endoscope is withdrawn with the inflated balloon; the colonoscopy is performed with the support of the CADEYE system for polyp detection in both insertion and withdrawal phase; all polyps identified are removed and sent for histopathology examination. Individuals randomized in the Control arm (CADEYE only) receive colonoscopy with G-EYE 760R colonoscope but the balloon remains deflated for the entire procedure; the colonoscopy is performed with the support of the CADEYE system for polyp detection in both insertion and withdrawal phase; all polyps identified are removed and sent for histopathology examination. The main outcome measure is the rate of "high risk" individuals across the two study arms.

It is estimated that about 20% of colonoscopies have inadequate preparation. (5) This is associated with lengthy procedures and less detection of adenomas, reduces the screening intervals, and increases the costs and risks of complications. Several strategies have been proposed to improve the quality of bowel preparation. Mobile healthcare Apps have been developed to increase adherence to bowel preparation agents, improving the quality of bowel preparation. However, adherence to mobile healthcare Apps is also a quality criterion and a pending problem to solve with this new technology. GastroBot is a new technology based on artificial intelligence that allows, through a software bot, to carry out a personalized follow-up of the patient's bowel cleansing, advising the patient to overcome contingencies that arise with the preparation, which in other circumstances could lead to the failure of it. The primary aim of this study is to determine the improvement in bowel preparation after GastroBot assistance compared with the traditional explanation. As a secondary aim, this study also pursues to determine adenoma and polyp detection rates (ADR and PDR, respectively), bowel preparation agents' tolerance, and GastroBot functionality.

Colonoscopy is a common endoscopic procedure which the UVA Gastroenterology & Hepatology department performs on a daily basis for both screening/surveillance and diagnostic/therapeutic intent. As a safety net hospital, the institution also offers open access colonoscopy as a means of allowing outside primary care and urgent care providers a way to have patients receive necessary endoscopy services. However, these patients are not all seen in clinic prior to their procedure but are all prescribed a specific bowel prep by a medical professional who screens the referrals. Given that the patient population described above are often not well-known to UVA providers, they may be prescribed prep regimens that are not ideal for their comorbidities. Not infrequently, these patients show up to their colonoscopies with an inadequate bowel prep, leading to either cancelled or incomplete procedures, increased healthcare and personal financial costs (such as missed wages from taking off work), suboptimal endoscopy resource utilization, and delay in or missed polyp (or potentially cancer) detection. The investigators seek to use a novel scoring system designed to predict patients at risk for suboptimal preps (and hence patients that would benefit from an extended prep) and apply it in uniform fashion to a patient population most at risk for suboptimal preps. Because obtaining written consent is not practical given that these patients may not be seen by a UVA provider prior to their endoscopic procedure, the investigators will plan to verbally consent via the telephone in an all-inclusive manner; there will be no randomization but rather the selection of prep to be determined by the novel scoring system. Subjects be prescribed either a standard split-dose GoLytely prep (or SuPrep) or an extended split-dose GoLytely prep. The investigators predict that utilizing this new system will lead to a decreased number of inadequate bowel preps and will increase polyp detection. The investigators will work with a UVA biostatistician to analyze the data and use Chi Square, student's T tests, and logistic regression models to assess significance and help validate the model.

The investigators want to verify in a randomised trial, the effectiveness of EndoClotTM in preventing post-procedural bleeding after EMR (Endoscopic Mucosal Resection) or ESD (Endoscopic Submucosal Dissection) for sessile lesions >20 mm in the right colon

Computer aided detection (CADe) algorithms have been developed to overcome human errors and assist endoscopists in detecting more polyps during colonoscopy. The aim of this study was to investigate the accuracy of the novel Pentax Discovery CADe system (Discovery-AI) against pre-recorded videos of colon polyps of various size, shape and pathology while using videos of normal colon segments as controls from two different institutes.

The endoscopist performances in the optical diagnosis (OD) of colonic polyps with the available technologies vary widely across centers and across endoscopists. The OD process is strictly related to the operator training and expertise. Most of the available studies in optical characterization have been carried out by expert endoscopist in tertiary high volume centers, and weren't replied on large unselected populations. For these reasons, at the moment the optical characterization of polypoid lesions can't replace, in the everyday clinical practice, the histopathological evaluation of resected polyps. Artificial intelligence (AI)-based systems have the potential to make optical characterization process of colonic polyps easier and more reliable, thus supporting the endoscopist in the application of leave-in-situ and of resect-and-discard strategies. The implementation of such strategies would lead to a significant economic saving and a decrease of risks and complications related to unnecessary polypectomy. GI-Genius System (Medtronic Inc, Minneaopolis, USA) is a CNN-based algorithm allowing an automatic OD of colonic polyps. This system does not require dedicated light setting for polyp evaluation as it works with white light high definition images, which are the actual standard in every endoscopic unit. During colonoscopy, when a polyp is framed within the screen, a green detection box surrounds the polyp and the system automatically provides (whenever possible) the optical diagnosis labeling the polyp as "adenoma or non-adenoma". When the automatic polyp charaterization is unfeasible the label "no prediction" appears. Nowadays only few data about the feasibility and performances of this system in clinical practice are available. In addition published studies are mostly focused on technical rather thann clinical issues. The present prospective observational trial is primarily aimed at evaluating the diagnostic accuracy of optical characterization of colonic polyps <= 1 cm using GI-Genius System in daily clinical practice, having histopathology examination as reference standard.

The aim of this project is to promote the breath volatile marker concept for colorectal cancer (CRC) screening by advancing developing the application of a novel hybrid analyzer for the purpose. The hybrid analyzer concept is expected to benefit of combining metal-oxide (MOX) and infrared spectrum (IR) sensor acquired data. The current study will be the first globally to address this concept in CRC detection. In addition, traditional methods, in particular, gas chromatography coupled to mass spectrometry (GC-MS) will be used to address the biological relevance of the VOCs emission from cancer tissue and will assist in further advances of the hybrid-sensing approach.

This study will be a prospective analysis conducted by Geneoscopy Inc. to evaluate the Colosense test, which is a multi-target stool RNA test for colorectal screening.

With this study, the investigators plan to review the performance of colonoscopic resection of large (> 1 cm) and flat lesions in the colon. The findings will help us define the role of colonoscopic resection of colon polyps and flat lesions and identity areas for improvement. The data will be used for continuing quality improvement and presenting our outcomes at academic meetings and publishing our results in peer reviewed journals.