View clinical trials related to Colonic Polyp.
Filter by:Purpose & Research Questions The purpose of this study is to evaluate whether artificial intelligence (AI) improves the detection of polyps and whether the system can classify the type and severity of detected changes. The investigators will also assess if there are any differences between the various AI systems and whether the polyps that may be missed are benign or malignant.
The goal of this observational study is to assess the correlation between the artificial intelligence (AI) derived effective withdrawal time (EWT) during colonoscopy and endoscopists' baseline adenoma detection rate (ADR). The association between the AI derived EWT with ADR during the prospective colonoscopy series would also be determined. The colonoscopy video of participants will be monitored by the AI and the result of EWT will be blinded to the endoscopists
Colonoscopic removal of adenomatous polyps reduce both the incidence and mortality of colorectal cancer (CRC). The common clinical management of colorectal polyp detected during colonoscopy is to remove them and send for histopathology to determine the subsequent surveillance interval. More than 80% of polyps detected during screening or surveillance colonoscopy are diminutive (≤5mm). As the chance of diminutive polyps to harbor cancer or advanced neoplasia is low, leave-in-situ and resect-and-discard strategies using optical diagnosis are recommended for non-neoplastic polyps by the American Society for Gastrointestinal Endoscopy (ASGE) and the European Society for Gastrointestinal Endoscopy (ESGE) so as to reduce the financial burden of polypectomy and histopathology. The societies proposed leave-in-situ strategy if optical diagnosis can achieve a negative predictive value (NPV) of >90% for rectosigmoid polyp and resect-and-discard if an agreement of more than 90% concordance with histopathology-based post-polypectomy surveillance interval can be achieved. However, optical diagnosis is operator dependent and most endoscopists are reluctant to adopt this strategy in routine practice because of the need of strict training and auditing and fear of incorrect diagnosis. In the past decade, with the exponential increase in computational power, reduced cost of data storage, improved algorithmic sophistication, and increased availability of electronic health data, artificial intelligence (AI) assisted technologies were widely adopted in various healthcare settings to improve clinical outcomes, especially the quality of colonoscopy in the area of gastroenterology. Real time use of computer-aided diagnosis (CADx) for adenoma using AI systems were developed and proven to be useful to help endoscopists to distinguish neoplastic polyps from non-adenomatous polyps. However, these studies only examined diminutive polyp but not polyp of larger size (>5mm). They were conducted with small sample size of less than few hundred subjects and the study settings were open-label and non-randomized. The investigators aim to conduct a large scale randomized controlled trial to evaluate the performance of colorectal polyp characterization of all size polyps by real-time CADx using AI system against conventional colonoscopy with optical diagnosis.
Cold polypectomy has the advantages of simple operation, less time-consuming and fewer complications. Guidelines have recommended cold snare polypectomy (CSP) to resect small polyps sized <9 mm. CSP was designed to improve the complete resection rate and reduce adverse events. Investigators hypothesize that CSP is better than conventional hot snare endoscopic mucosal resection (HS-EMR) in the presence of injured submucosal arteries detected in the submucosal layer for 10-19 mm nonpedunculated colorectal polyps, resulting in lower delayed bleeding after CSP of 10-19 mm nonpedunculated colorectal polyps.
The goal of this trial is to determine whether use of a Computer Assisted Detection (CADe) programme leads to an increase in ADR for either units or individual colonoscopists, independent of setting or expertise
The objective of this study is to collect colonoscopy data for use in the development and testing of artificial intelligence (AI) devices for colonoscopies.
CCIS is a novel score, created specifically to evaluate the completeness of caecal visualized. It can be applied to a single or multiple images. To create the CCIS, the caecum was divided into eight parts: the appendiceal orifice (AO), the tri-radiate fold part 1 (TF-1), 2 (TF-2), 3 (TF-3) and four outer quadrants (OQ 1-4). The ileo-caecal valve (ICV) is a reference point but is not part of the score. The quadrant adjacent to the ICV is labelled OQ1. The three other quadrants are labelled clockwise from this quadrant. The tri-radiate folds are also labelled clockwise with TF1 representing the triangle side that is majority-contained within OQ1. TF2 and TF3 are then labelled clockwise from TF1.
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.
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
To conduct an single blinded, non-randomized, prospective, single center trial to validate the performance of a novel state-of-the-art Artificial Intelligence model (AI-Model) for colorectal lesion detection during routine diagnostic colonoscopy and to evaluate its feasibility in daily endoscopy. Consecutive patients referred for a screening, surveillance or diagnostic colonoscopy will be included