Artificial Intelligence for Real-time Detection and Monitoring of Colorectal Polyps
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
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);
Study Type
Study Type
Enrollment (Actual)
Enrollment
Phase
Phase
- Not Applicable
Contacts and Locations
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Genders Eligible for Study
Description
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
Study Plan
How is the study designed?
Design Details
- Primary Purpose: DIAGNOSTIC
- Allocation: NA
- Interventional Model: SINGLE_GROUP
- Masking: NONE
Number of Arms
Arms and Interventions
Participant Group / ArmParticipant Group / Arm |
Intervention / TreatmentIntervention / 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.
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.
|
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
|
What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Number of polyps detected
Time Frame: Day 1
|
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
Time Frame: Day 1
|
Compare of the automatic detection of the ileocecal valve, appendiceal orifice, and the automatic calculation of the withdrawal time with manual detection
|
Day 1
|
Collaborators and Investigators
Sponsor
Sponsor
Study record dates
Study Major Dates
Study Start (ACTUAL)
Study Start
Primary Completion (ACTUAL)
Primary Completion
Study Completion (ACTUAL)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (ACTUAL)
First Posted
Study Record Updates
Last Update Posted (ACTUAL)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Keywords
Additional Relevant MeSH Terms
Other Study ID Numbers
Other Study ID Numbers
- 20.198
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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