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

NCT04586556 | Completed

• 1 other identifier: 20.198
• Study Type: interventional
• Target: 372
• Locations: 2 countries
• Sites: 3

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).

Conditions

Adenomatous Polyps

Keywords

Polyps detection; Artificial Intelligence; Adenoma detection; Polyps classification; Quality indicators

Outcome Measures

Primary Outcomes (2)

• 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.

  Day 1

• 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

  Day 1

Study Arms (1)

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

EXPERIMENTAL

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.

Diagnostic Test: Polyps detection by Artificial Intelligence

Interventions

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

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

Eligibility Criteria

• Age: 45 Years - 80 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Signed informed consent
• Age 45-80 years
• Indication to undergo a lower GI endoscopy.

You may not qualify if:

• 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

Sponsors & Collaborators

• Centre hospitalier de l'Université de Montréal (CHUM): lead

Study Sites (3)

Université de Montréal

Montreal, Quebec, QC H3T 1J4, Canada

Location

Centre Hospitalier Universitaire de Montréal

Montreal, Quebec, Canada

Location

IHU Strasbourg

Strasbourg, 67000, France

Location

MeSH Terms

Conditions

Adenomatous Polyps

Condition Hierarchy (Ancestors)

Adenoma; Neoplasms, Glandular and Epithelial; Neoplasms by Histologic Type; Neoplasms

Study Officials

• Daniel von Renteln

  Centre hospitalier de l'Université de Montréal (CHUM)

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

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

Study Record Dates

• First Submitted: October 1, 2020
• First Posted: October 14, 2020
• Study Start: December 18, 2020
• Primary Completion: March 31, 2022
• Study Completion: May 11, 2022
• Last Updated: November 25, 2022

Record last verified: 2022-11

Data Sharing

• IPD Sharing: Will not share

Locations

CA (2); FR (1)