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

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).

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Key information

Conditions

Adenomatous Polyps Adenoma Neoplasms Neoplasms by Histologic Type Neoplasms, Glandular and Epithelial

Age range

45 year–80 year

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

Centre Hospitalier Universitaire de Montréal, Montreal, Quebec, Canada

About this study

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);

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

Polyps detection by Artificial Intelligence

Diagnostic Test

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

Primary outcomes

Number of polyps detected

Time frame: 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

Time frame: Day 1

Compare of the automatic detection of the ileocecal valve, appendiceal orifice, and the automatic calculation of the withdrawal time with manual detection

Sponsors and collaborators

Lead sponsor

Centre hospitalier de l'Université de Montréal (CHUM)

Other

Registry information

Important dates

Study start: 2020
Primary completion: 2022
Study completion: 2022
First posted: Oct 14, 2020
Registry last updated: Nov 25, 2022

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

Published trials that share one or more normalized conditions with this study.

Completed