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Brief Title: Artificial Intelligence for Real-time Detection and Monitoring of Colorectal Polyps
Official Title: Artificial Intelligence for Real-time Detection and Monitoring of Colorectal Polyps
Study ID: NCT04586556
Brief Summary: The investigators hypothesize that the clinical implementation of a deep learning AI system is an optimal tool to monitor, audit and improve the detection and classification of polyps and other anatomical landmarks during colonoscopy. The objectives of this study are to generate preliminary data to evaluate the effectiveness of AI-assisted colonoscopy on: a) the rate of detection of adenomas; b) the automatic detection of the anatomical landmarks (i.e., ileocecal valve and appendiceal orifice).
Detailed Description: In this trial, the investigators aim to evaluate the followings: 1. the accuracy of automatic detection of important anatomical landmarks (i.e., ileocecal valve, appendiceal orifice); 2. the accuracy of automatic detection of polyps/adenomas (PDR/ADR);
Minimum Age: 45 Years
Eligible Ages: ADULT, OLDER_ADULT
Sex: ALL
Healthy Volunteers: No
Université de Montréal, Montréal, Quebec, Canada
Centre Hospitalier Universitaire de Montréal, Montréal, Quebec, Canada
IHU Strasbourg, Strasbourg, , France
Name: Daniel von Renteln
Affiliation: Centre hospitalier de l'Université de Montréal (CHUM)
Role: PRINCIPAL_INVESTIGATOR