Artificial Intelligence for Real-time Detection and Monitoring of Colorectal Polyps

Study Overview
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Brief Summary
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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
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In this trial, the investigators aim to evaluate the followings: the accuracy of automatic detection of important anatomical landmarks (i.e., ileocecal valve, appendiceal orifice); the accuracy of automatic detection of polyps/adenomas (PDR/ADR);

Official Title
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Artificial Intelligence for Real-time Detection and Monitoring of Colorectal Polyps

Conditions
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Adenomatous Polyps

Intervention / Treatment
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* Diagnostic Test: Polyps detection by Artificial Intelligence


Participation Criteria
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Eligibility Criteria
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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

Ages Eligible for Study
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Minimum Age: 45 Years
Maximum Age: 80 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Diagnostic
Intervention Model: Single Group Assignment
Interventional Model Description: prospective, multi-endoscopist, single center, clinical study at tertiary referral center (CHUM)
Masking: None (Open Label)


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: Artificial intelligence for real-time detection and monitoring of colorectal polyps<br>A standard colonoscopy will be performed according to the standard of routine care. All optically diagnosed polyps will be removed and sent to the CHUM pathology laboratory for histopathological evaluation according to institutional standards. The AI system will capture video of the procedure in real time, and provide additional information on the detection of polyps, follow-up and prediction of pathology. The full-length colonoscopy videos will be annotated for the exact time of the identification of the anatomical landmarks, polyps, also for polyp- and procedural-related characteristics. | Diagnostic Test: Polyps detection by Artificial Intelligence<br>* 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<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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. | 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 | Day 1 | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Polyps detection, Artificial Intelligence, Adenoma detection, Polyps classification, Quality indicators