Adenomatous Polyps
Conditions
Keywords
Polyps detection, Artificial Intelligence, Adenoma detection, Polyps classification, Quality indicators
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);
Interventions
DIAGNOSTIC_TESTPolyps detection by Artificial Intelligence
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
Sponsors
Centre hospitalier de l'Université de Montréal (CHUM)
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
DIAGNOSTIC
Masking
NONE
Intervention model description
prospective, multi-endoscopist, single center, clinical study at tertiary referral center (CHUM)
Eligibility
Sex/Gender
ALL
Age
45 Years to 80 Years
Healthy volunteers
No
Inclusion criteria
: * Signed informed consent * Age 45-80 years * Indication to undergo a lower GI endoscopy.
Exclusion criteria
: * Coagulopathy * Poor general health, defined as an American Society of Anesthesiologists (ASA) physical status class \>3 * Emergency colonoscopies * Hospitalized patients * Known inflammatory bowel disease (IBD) * Patients currently in the emergency room
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Number of polyps detected | Day 1 | Efficacy of AI assisted colonoscopy to detect the proportion of patients with at least 1 polyp. Polyp detection rate with an AI. |
| Evaluation of the automatic report of the colonoscopy quality indicators | Day 1 | Compare of the automatic detection of the ileocecal valve, appendiceal orifice, and the automatic calculation of the withdrawal time with manual detection |
Countries
Canada, France
Outcome results
None listed