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

Primary Purpose

Adenomatous Polyps

Status
Completed
Phase
Not Applicable
Locations
International
Study Type
Interventional
Intervention
Polyps detection by Artificial Intelligence
Sponsored by
Centre hospitalier de l'Université de Montréal (CHUM)
About
Eligibility
Locations
Arms
Outcomes
Full info

About this trial

This is an interventional diagnostic trial for Adenomatous Polyps focused on measuring Polyps detection, Artificial Intelligence, Adenoma detection, Polyps classification, Quality indicators

Eligibility Criteria

45 Years - 80 Years (Adult, Older Adult)All SexesDoes not accept healthy volunteers

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

Sites / Locations

  • Université de Montréal
  • Centre Hospitalier Universitaire de Montréal
  • IHU Strasbourg

Arms of the Study

Arm 1

Arm Type

Experimental

Arm Label

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

Arm Description

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.

Outcomes

Primary Outcome Measures

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.
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

Secondary Outcome Measures

Full Information

First Posted
October 1, 2020
Last Updated
November 23, 2022
Sponsor
Centre hospitalier de l'Université de Montréal (CHUM)
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1. Study Identification

Unique Protocol Identification Number
NCT04586556
Brief Title
Artificial Intelligence for Real-time Detection and Monitoring of Colorectal Polyps
Official Title
Artificial Intelligence for Real-time Detection and Monitoring of Colorectal Polyps
Study Type
Interventional

2. Study Status

Record Verification Date
November 2022
Overall Recruitment Status
Completed
Study Start Date
December 18, 2020 (Actual)
Primary Completion Date
March 31, 2022 (Actual)
Study Completion Date
May 11, 2022 (Actual)

3. Sponsor/Collaborators

Responsible Party, by Official Title
Sponsor
Name of the Sponsor
Centre hospitalier de l'Université de Montréal (CHUM)

4. Oversight

Studies a U.S. FDA-regulated Drug Product
No
Studies a U.S. FDA-regulated Device Product
No
Data Monitoring Committee
No

5. Study Description

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: 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);

6. Conditions and Keywords

Primary Disease or Condition Being Studied in the Trial, or the Focus of the Study
Adenomatous Polyps
Keywords
Polyps detection, Artificial Intelligence, Adenoma detection, Polyps classification, Quality indicators

7. Study Design

Primary Purpose
Diagnostic
Study Phase
Not Applicable
Interventional Study Model
Single Group Assignment
Model Description
prospective, multi-endoscopist, single center, clinical study at tertiary referral center (CHUM)
Masking
None (Open Label)
Allocation
N/A
Enrollment
372 (Actual)

8. Arms, Groups, and Interventions

Arm Title
Artificial intelligence for real-time detection and monitoring of colorectal polyps
Arm Type
Experimental
Arm Description
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.
Intervention Type
Diagnostic Test
Intervention Name(s)
Polyps detection by Artificial Intelligence
Intervention Description
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 Outcome Measure Information:
Title
Number of polyps detected
Description
Efficacy of AI assisted colonoscopy to detect the proportion of patients with at least 1 polyp. Polyp detection rate with an AI.
Time Frame
Day 1
Title
Evaluation of the automatic report of the colonoscopy quality indicators
Description
Compare of the automatic detection of the ileocecal valve, appendiceal orifice, and the automatic calculation of the withdrawal time with manual detection
Time Frame
Day 1

10. Eligibility

Sex
All
Minimum Age & Unit of Time
45 Years
Maximum Age & Unit of Time
80 Years
Accepts Healthy Volunteers
No
Eligibility Criteria
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
Overall Study Officials:
First Name & Middle Initial & Last Name & Degree
Daniel von Renteln
Organizational Affiliation
Centre hospitalier de l'Université de Montréal (CHUM)
Official's Role
Principal Investigator
Facility Information:
Facility Name
Université de Montréal
City
Montréal
State/Province
Quebec
ZIP/Postal Code
QC H3T 1J4
Country
Canada
Facility Name
Centre Hospitalier Universitaire de Montréal
City
Montréal
State/Province
Quebec
Country
Canada
Facility Name
IHU Strasbourg
City
Strasbourg
ZIP/Postal Code
67000
Country
France

12. IPD Sharing Statement

Plan to Share IPD
No

Learn more about this trial

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

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