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).
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);
Study Type
INTERVENTIONAL
Allocation
NA
Purpose
DIAGNOSTIC
Masking
NONE
Enrollment
372
The AI system will capture the live video of the procedure and the AI feedback (polyp detection, tracking, and pathology prediction) will be shown on a second screen installed next to the regular endoscopy screen. Screen A will show the regular endoscopy image and screen B will show the regular endoscopy image together with the areas that might harbor a polyp or the information to predict pathology
Université de Montréal
Montreal, Quebec, Canada
Centre Hospitalier Universitaire de Montréal
Montreal, Quebec, Canada
IHU Strasbourg
Strasbourg, France
Number of polyps detected
Efficacy of AI assisted colonoscopy to detect the proportion of patients with at least 1 polyp. Polyp detection rate with an AI.
Time frame: Day 1
Evaluation of the automatic report of the colonoscopy quality indicators
Compare of the automatic detection of the ileocecal valve, appendiceal orifice, and the automatic calculation of the withdrawal time with manual detection
Time frame: Day 1
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