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

Centre hospitalier de l'Université de Montréal (CHUM)

Status: Completed

Conditions: Adenomatous Polyps

Treatments: Diagnostic Test: Polyps detection by Artificial Intelligence

Study type: Interventional

Funder types: Other

Identifiers

NCT04586556

20.198

Details and patient eligibility

About

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).

Full description

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);

Enrollment: 372 patients

Sex: All

Ages: 45 to 80 years old

Volunteers: No Healthy Volunteers

Inclusion and exclusion 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

Trial design

Primary purpose: Diagnostic

Allocation: N/A

Interventional model: Single Group Assignment

Masking: None (Open label)

---

372 participants in 1 patient group

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

Experimental group

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.

Treatment:

Diagnostic Test: Polyps detection by Artificial Intelligence