View clinical trials related to Adenoma Colon.
Filter by:Deep learning technology has an increasing role in medical image applications and, recently, an artificial intelligence device has been developed and commercialized by Medtronic for identification of polyps during colonoscopy (GI-GENIUS). This kind of computer-aided detection (CADe) devices have demonstrated its ability for improving polyp detection rate (PDR) and the adenoma detection rate (ADR). However, this increase in PDR and ADR is mainly made at the expense of small polyps and non advanced adenomas. Colonoscopies after a positive fecal immunochemical test (FIT) could be the scenario with a higher prevalence of advanced lesions which could be the ideal situation for demonstrating if these CADe systems are able also to increase the detection of advanced lesions and which kind of advanced lesions are these systems able to detect. The CADILLAC study will randomize individuals within the population-based Spanish colorectal cancer screening program to receive a colonoscopy where the endoscopist is assisted by the GI-GENIUS device or to receive a standard colonoscopy. If our results are positive, that could suppose a big step forward for CADe devices, in terms of definitive demonstration of being of help for efectively identify also advanced lesions.
We intend to develop intelligent quality control and management software based on the work of digestive endoscopic artificial intelligence in the Department of Gastroenterology, Wuhan University Renmin Hospital, to accurately and comprehensively assess the quality of gastrointestinal endoscopy and to provide a practical basis for improving the quality of digestive endoscopy in our endoscopic center.
The Endocuff study aims to show an increase of the number of detected adenomas in screening colonoscopies when a endocuff vision (Arc Medical Design Ldt Leeds England) is used.
Our previous study (Am. J. Gastroenterol. 2017 04;112(4)) showed that water exchange (WE) colonoscopy was able to increase the detection of flat adenomas as well as overall ADR. We hypothesized that WE based whole-colon acetic acid（AA） staining might be useful to improve the detection of flat lesions compared with WE alone.
This study compares the effectiveness in complete resection (absence of recurrence at 6 months) the two different techniques for performing endoscopic mucosal resection (EMR) of nonpedunculated homogeneous colorectal lesions >20mm
In this observational pilot study, we assess the diagnostic performance of an artificial intelligence sytem for automated detection of colorectal polyps.
Background: Colonoscopy is accepted to be the gold standard for screening of colorectal cancer (CRC). Most CRCs develop from adenomatous polyps, with colonoscopy accepted to be the gold standard for screening of CRC. An endoscopist's ability to detect polyps is assessed in the form of an Adenoma Detection Rate (ADR). Each 1.0% increase in ADR is associated with a 3.0% decrease in the risk of the patient developing an interval CRC. There remains a wide variation in endoscopist ADR. More recently, the use of artificial intelligence (AI) and computer aided diagnosis in endoscopy has been gaining increasing attention for its role in automated lesion detection and characterisation. AI can potentially improve ADR, but previous AI related work has largely focused on retrospectively assessing still endoscopic images and selected video sequences which may be subject to bias and lack clinical utility. There are only limited clinical studies evaluating the effect of AI in improving ADR. The CADDIE device uses convolutional neural networks developed for computer assisted detection and computer assisted diagnosis of polyps. Primary objective: To determine whether the CADDIE artificial intelligence system improves endoscopic detection of adenomas during colonoscopy. Primary endpoint: The difference in adenoma detection rate (ADR) between the intervention (supported with the CADDIE system) and non-intervention arm Study design: Multi-Centre, open-label, randomised, prospective trial to assess efficacy and safety of the CADDIE artificial intelligence system for improving endoscopic detection of colonic polyps in real-time.
Colorectal cancer is one of the most common cancer in Hong Kong. In 2018, CRC accounted for 17.4%, 5,780 cases, of the total new cancers. CRC claimed 2,279 lives (15.8%) making it the second most deadly killer in the population. Since 2010, the Cancer Expert Working Group (CEWG) has recommended that asymptomatic average-risk individuals aged 50 to 75 years should consider one of the screening methods: fecal occult blood test (FOBT) every one to two years; OR flexible sigmoidoscopy every 5 years; OR colonoscopy every 10 years. However, it poses great challenges for large scale CRC screening using colonoscopy, such as bowel preparation difficulties, complications of procedure and poor compliance. ColoClear® is intended for use as an adjunctive screening test for the detection of colorectal neoplasia associated DNA markers and for the presence of occult hemoglobin in human stool. It has the potential of increasing the sensitivity of detecting CRC as compared to FOBT or faecal immunochemical test (FIT), which detects the presence of hemoglobin in stool alone. A positive result may indicate the presence of colorectal cancer or pre-malignant colorectal neoplasia. ColoClear® is not intended as a replacement for diagnostic colonoscopy. A positive result in ColoClear®, as with any screening test, should be followed by colonoscopy. ColoClear® is intended for colorectal cancer screening in average risk individuals: adults of either sex, 40 years or older, who are at high risk for colorectal cancer.
Comparing the complete resection rate and subsequent adenoma recurrence rate at surveillance colonoscopy of 15-40mm laterally spreading adenomas for conventional EMR vs. cold snare EMR.
Comparison of the number of adenomas missed by the first exploration of the right colon by LCI (linked color imaging) or by white light during a colonoscopy, according to the order of randomization.