Recruiting

AI-Assisted Diagnosis

Sponsor:

Daniel Von Renteln

Code:

NCT06059378

Conditions

Artificial Intelligence

Eligibility Criteria

Sex: All

Age: 45 - 70+

Healthy Volunteers: Not accepted

Interventions

Artificial intelligence-assisted classification (CADx)

Study Details

Brief summary:

This is a prospective study that is the first to implement resect and discard and diagnose and leave strategies in real-time practice using stringent documentation and adjudication by 2 expert endoscopists as the gold standard.

The primary aim of this study is to show the accuracy of intracolonoscopy AI-assisted optical diagnosis (CADx; autonomous or with human input) when the AI-assisted optical diagnosis made by the expert endoscopists is used as the reference standard. The specific aims are:

1. To evaluate the accuracy of intracolonoscopy AI-assisted optical polyp diagnosis (autonomous or with human input) by comparing it to the obtained optical histology diagnoses provided by two independent expert endoscopists as the reference standard.
2. To evaluate the agreement between the intracolonoscopy AI-assisted optical polyp diagnosis (autonomous or with human input) and the AI-assisted optical diagnosis performed by two independent expert endoscopists.
3. To determine whether AI-assisted optical polyp diagnosis for diminutive (1-5 mm) polyps can be implemented in routine clinical practice by demonstrating that at least 70% of the approached patients are interested in undergoing AI-assisted optical diagnosis (autonomous or with human input).
4. To evaluate the cost savings resulting from replacing pathology with AI-assisted optical diagnosis.

Conditions

Artificial Intelligence

Study ID

NCT06059378

Start date

Sep 1, 2023

Status verified date

Feb, 2025

Completion date

Jun 30, 2025

Anticipated

Primary completion date

May 1, 2025

Anticipated

Eligibility Criteria

Eligibility Criteria

Sex: All

Age: 45 - 70+

Healthy Volunteers: Not accepted

Inclusion Criteria:

  • Age 45-80 years
  • Undergoing an outpatient colonoscopy at the Centre Hospitalier de l'Université de Montréal (CHUM)
  • Signed informed consent form

Exclusion Criteria:

  • Inflammatory Bowel Disease;
  • Active colitis;
  • Hereditary CRC syndrome;
  • Coagulopathy;
  • American Society of Anesthesiologists (ASA) status >3

Study Design

Enrollment

204 participants

Anticipated

Allocation

Non randomized

Intervention Model

Parallel Assignment

Primary purpose

Diagnostic

Interventions and Outcome Measures

Arms

other: AI-assisted classification with endoscopist's input

AI-assisted classification for diminutive polyps during a colonoscopy procedure using the CAD-eye detection and classification system, with input from the endoscopist in the case of serrated polyps, for patients who agree to undergo optical diagnosis of diminutive colorectal polyps.

other: Autonomous AI-assisted classification

AI-assisted classification for diminutive polyps during a colonoscopy procedure using the CAD-eye detection and classification system, with no input from the endoscopist, for patients who agree to undergo optical diagnosis of diminutive colorectal polyps.

Interventions

Artificial intelligence-assisted classification (CADx)

CADeye (Fujifilm, Japan) is a joint detection (CADe) and classification (CADx) AI-supported system, which has been developed utilising AI deep learning technology to support endoscopic lesion detection and characterisation in the colon.

Primary outcome measure

  • Accuracy of the intracolonoscopy AI-assisted optical diagnosis [ Time Frame: 120 days ]

Central Contacts and Locations

Locations

Centre Hospitalier de l'Université de Montréal

Recruiting

Montréal, Quebec, Canada

Contacts

Principal Investigator:

Daniel Von Renteln, MD

More Information

Sponsor

Daniel Von Renteln

Last update posted

Feb 19, 2025

Last verified

Feb, 2025

Keywords

  • Artificial intelligenc
  • Optical diagnosis
  • Colonoscopy
  • Resect and Discard

Trial information was received from ClinicalTrials.gov and was last updated on 2026-09-10. This information was provided to ClinicalTrials.gov by Daniel Von Renteln on 2025-02-19.