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Colorectal Adenomatous Polyp clinical trials

View clinical trials related to Colorectal Adenomatous Polyp.

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NCT ID: NCT05477836 Completed - Colorectal Cancer Clinical Trials

Feasibility and Safety of MiWEndo-assisted Colonoscopy

MiWEndo1
Start date: October 18, 2022
Phase: N/A
Study type: Interventional

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).

NCT ID: NCT04723758 Completed - Colorectal Adenoma Clinical Trials

COLO-DETECT: Can an Artificial Intelligence Device Increase Detection of Polyps During Colonoscopy?

Start date: March 29, 2021
Phase: N/A
Study type: Interventional

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.

NCT ID: NCT04192565 Completed - Clinical trials for Colorectal Neoplasms

A Prospective Investigation of the ColubrisMX ELS System

Start date: September 9, 2020
Phase: N/A
Study type: Interventional

This study is a prospective, single-arm, open-label, multi-center, feasibility study to evaluate the safety and efficacy of ColubrisMX ELS System in patients undergoing transanal endoluminal procedures, specifically colorectal Endoscopic Submucosal Dissection.

NCT ID: NCT03234725 Completed - Colorectal Adenoma Clinical Trials

Analysis of New Endoscopic Features and Variable Stiffness in Colonoscopy: Prospective Randomised Trial

ELUFIBLI
Start date: October 1, 2016
Phase:
Study type: Observational

The aim of the present study is to develop and evaluate a computer-based methods for automated and improved detection and classification of different colorectal lesions, especially polyps. For this purpose first, pit pattern and vascularization features of up to 1000 polyps with a size of 10 mm or smaller will be detected and stored in our web based picture database made by a zoom BLI colonoscopy. These polyps are going to be imaged and subsequently removed for histological analysis. The polyp images are analyzed by a newly developed deep learning computer algorithm. The results of the deep learning automatic classification (sensitivity, specificity, negative predictive value, positive predictive value and accuracy) are compared to those of human observers, who were blinded to the histological gold standard. In a second approach we are planning to use LCI of the colon, rather than the usual white light. Here, we will determine, whether this technique could improve the detection of flat neoplastic lesions, laterally spreading tumors, small pedunculated adenomas and serrated polyps. The polyps are called serrated because of their appearance under the microscope after they have been removed. They tend to be located up high in the colon, far away from the rectum. They have been definitely shown to be a type of precancerous polyp and it is possible that using LCI will make it easier to see them, as they can be quite difficult to see with standard white light.