Recruiting

AI-Enhanced Endoscopy

Sponsor:

Centre hospitalier de l'Université de Montréal (CHUM)

Code:

NCT06543862

Conditions

Colonic Polyp

Artificial Intelligence

Eligibility Criteria

Sex: All

Age: 45 - 70+

Healthy Volunteers: Not accepted

Interventions

CADx (AI) system

Study Details

Brief summary:

Computer-aided image-enhanced endoscopy can predict the nature of colorectal polyps with over 90% accuracy. This technology uses artificial intelligence (AI) to analyze video recordings of polyps, learning to make diagnoses in real-time. This means that doctors can get immediate predictions about small polyps during the procedure, reducing the need for separate pathology exams and saving costs, ultimately improving patient care.

Human and AI interactions are complex and a framework to reap synergistic effects CADx systems when used by humans to harness optimal performance needs to be established. AI solutions in medicine are usually developed to be used as assistive devices, however, then they rely on humans to correct AI errors. Optical polyp diagnosis is a complex task. Non experts usually achieve diagnostic accuracy in 70-80%. CADx systems have a similar diagnostic accuracy when used autonomously. Clinical evaluation of CADx systems showed that CADx assisted OD performs equally to the operator performance when using non CADx assisted OD. To harness a benefit of clinical CADx implementation we would have to find a way that synergies between human and CADx come into play to eliminate cases in which CADx assisted and/ or human OD results in low diagnostic accuracy and also addresses the problem of serrated polyp recognition.

Conditions

Colonic Polyp

Artificial Intelligence

Study ID

NCT06543862

Start date

Nov 15, 2024

Status verified date

Sep, 2026

Completion date

Oct, 2026

Anticipated

Primary completion date

Oct, 2026

Anticipated

Eligibility Criteria

Eligibility Criteria

Sex: All

Age: 45 - 70+

Healthy Volunteers: Not accepted

Inclusion Criteria:

  • Indication for full colonoscopy.

Exclusion Criteria:

  • Known inflammatory bowel disease
  • Active colitis
  • coagulopathy
  • familial polyposis syndrome
  • poor general health, defined as an American Society of Anesthesiologists class >3
  • emergency colonoscopy

Study Design

Enrollment

540 participants

Anticipated

Intervention Model

Single group

Primary purpose

Diagnostic

Interventions and Outcome Measures

Arms

other: All participants

The endoscopist will make an optical diagnosis (OD) prediction for all small polyps (up to 10 mm) in white light (WL). Then, the endoscopist will make another OD prediction using image enhanced endoscopy (IEE) modes. After that, CADx will be activated in the IEE mode and a CADx prediction will be documented. Finally, after seeing the CADx prediction, the endoscopist will make a final prediction, which can agree or disagree with the autonomous CADx one. Polyps will be resected and sent to a pathology lab, where a pathologic diagnosis (blinded to the endoscopist's predictions) will be rendered.

Interventions

CADx (AI) system

The CADx system will be used to predict the histopathology of the polyp detected.

Primary outcome measure

  • Accuracy of optical diagnosis, for polyps 1-5mm, compared with an agreed upon CADx-assisted diagnosis [ Time Frame: up to 100 weeks ]

Central Contacts and Locations

Central contacts

Locations

Ghislaine Ahoua

Recruiting

Montreal, Quebec, Canada

More Information

Sponsor

Centre hospitalier de l'Université de Montréal (CHUM)

Last update posted

Sep 21, 2026

Last verified

Sep, 2026

Keywords

  • optical diagnosis, artificial intelligence

Trial information was received from ClinicalTrials.gov and was last updated on 2026-09-24. This information was provided to ClinicalTrials.gov by Centre hospitalier de l'Université de Montréal (CHUM) on 2026-09-21. Recruitment status is synced daily from ClinicalTrials.gov and may not reflect the sponsor's current status. Confirm during your call.