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

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ClinicalTrials.gov Identifier: NCT04586556
Recruitment Status : Recruiting
First Posted : October 14, 2020
Last Update Posted : March 24, 2021
Sponsor:
Information provided by (Responsible Party):
Centre hospitalier de l'Université de Montréal (CHUM)

Brief Summary:
The investigators hypothesize that the clinical implementation of an AI system is an optimal tool to monitor, audit and improve the detection and classification of polyps 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 histological classification of polyps (which would reduce the need for pathological evaluation of polyps); c) the performance of endoscopists in detecting polyps and in the accuracy of polyp classification.

Condition or disease Intervention/treatment Phase
Adenomatous Polyps Diagnostic Test: Polyps detection by Artificial Intelligence Not Applicable

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Study Type : Interventional  (Clinical Trial)
Estimated Enrollment : 600 participants
Allocation: N/A
Intervention Model: Single Group Assignment
Intervention Model Description: prospective, multi-endoscopist, single center, clinical study at tertiary referral center (CHUM)
Masking: None (Open Label)
Primary Purpose: Diagnostic
Official Title: Artificial Intelligence for Real-time Detection and Monitoring of Colorectal Polyps
Actual Study Start Date : December 18, 2020
Estimated Primary Completion Date : November 2021
Estimated Study Completion Date : November 2021

Arm Intervention/treatment
Experimental: 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.
Diagnostic Test: 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




Primary Outcome Measures :
  1. Efficacy of detection of adenomas [ Time Frame: Day 1 ]
    Efficacy of AI assisted colonoscopy to detect the proportion of patients with at least 1 neoplastic polyp. Adenoma detection rate with an AI.


Secondary Outcome Measures :
  1. Incomplete resection rate (IRR) [ Time Frame: Day 1 ]
    proportion of incomplete polyp resections proportion of patients with either adenoma or carcinoma rate and type of adverse and serious adverse events occurring during the procedure.

  2. prediction of polyp histology [ Time Frame: Day 14 ]
    evaluate the agreement of the optical diagnosis-based surveillance interval with the pathology-based recommendation assessing the prediction accuracy of advanced neoplasia proportion of patients for whom an immediate surveillance recommendation following the colonoscopy can be directly provided when using AI-assisted optical diagnosis and how often histopathology polyp examination would have been avoided by using this strategy.



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Ages Eligible for Study:   45 Years to 80 Years   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
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

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT04586556


Locations
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Canada, Quebec
Université de Montréal Active, not recruiting
Montréal, Quebec, Canada, QC H3T 1J4
Centre Hospitalier Universitaire de Montréal Recruiting
Montréal, Quebec, Canada
Contact: Maureen Fontaine    (514) 890-8000 Ext. 30655    maureen.fontaine.chum@ssss.gouv.qc.ca   
Principal Investigator: Daniel von Renteln, MD         
Sub-Investigator: Simon Bouchard, MD         
Sub-Investigator: Erik Deslandres, MD         
Sub-Investigator: Mickael Bouin, MD         
Sub-Investigator: Audrey Weber, MD         
Sub-Investigator: Sacha Sidani, MD         
France
IHU Strasbourg Active, not recruiting
Strasbourg, France, 67000
Sponsors and Collaborators
Centre hospitalier de l'Université de Montréal (CHUM)
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Responsible Party: Centre hospitalier de l'Université de Montréal (CHUM)
ClinicalTrials.gov Identifier: NCT04586556    
Other Study ID Numbers: 20.198
First Posted: October 14, 2020    Key Record Dates
Last Update Posted: March 24, 2021
Last Verified: October 2020
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: No

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Keywords provided by Centre hospitalier de l'Université de Montréal (CHUM):
Polyps detection
Artificial Intelligence
Adenoma detection
Polyps classification
Additional relevant MeSH terms:
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Adenomatous Polyps
Polyps
Pathological Conditions, Anatomical
Adenoma
Neoplasms, Glandular and Epithelial
Neoplasms by Histologic Type
Neoplasms