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

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Clinical Phase

NA

Total Enrollment

372 participants

Study Classification

INTERVENTIONAL

Study Start Date

2020-12-18

Study Completion Date

2022-05-11

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).

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Detailed Description

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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);

Conditions

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Adenomatous Polyps

Study Design

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Allocation Method

NA

Intervention Model

SINGLE_GROUP

prospective, multi-endoscopist, single center, clinical study at tertiary referral center (CHUM)

Primary Study Purpose

DIAGNOSTIC

Blinding Strategy

NONE

Study Groups

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Artificial intelligence for real-time detection and monitoring of colorectal polyps

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.

Group Type EXPERIMENTAL

Polyps detection by Artificial Intelligence

Intervention Type 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

Interventions

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Polyps 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

Intervention Type DIAGNOSTIC_TEST

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

Minimum Eligible Age

45 Years

Maximum Eligible Age

80 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No