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

Not Applicable

Completed

• Conditions: Adenomatous Polyps
• Interventions: Diagnostic Test: Polyps detection by Artificial Intelligence

• Registration Number: NCT04586556
• Lead Sponsor: Centre hospitalier de l'Université de Montréal (CHUM)

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);

Study Timeline

• Study First Submitted: 2020-10-01
• Study First Submitted QC: 2020-10-07
• Study First Posted: 2020-10-14
• Study Start: 2020-12-18
• Primary Completion: 2022-03-31
• Study Completion: 2022-05-11
• Status Verified: 2022-11
• Last Update Submitted: 2022-11-23
• Last Update Posted: 2022-11-25

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 372

Inclusion Criteria

Not provided

Exclusion Criteria

Not provided

Study & Design

• Study Type: INTERVENTIONAL
• Study Design: SINGLE_GROUP

Arm && Interventions

Group	Intervention	Description
Artificial intelligence for real-time detection and monitoring of colorectal polyps	Polyps detection by Artificial Intelligence	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.

Primary Outcome Measures

Name	Time	Method
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

Trial Locations

Locations (3)

IHU Strasbourg; 🇫🇷 Strasbourg, France

Centre Hospitalier Universitaire de Montréal; 🇨🇦 Montréal, Quebec, Canada

Université de Montréal; 🇨🇦 Montréal, Quebec, Canada