Using AI-assisted Optical Polyp Diagnosis for Diminutive Colorectal Polyps

NCT06059378 | Recruiting

• 1 other identifier: 2024-11557/23.095
• Study Type: interventional
• Target: 204
• Locations: 1 country
• Sites: 1

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. 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. 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. 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. 4.To evaluate the cost savings resulting from replacing pathology with AI-assisted optical diagnosis.

Conditions

Artificial Intelligence

Keywords

Artificial intelligenc; Optical diagnosis; Colonoscopy; Resect and Discard

Outcome Measures

Primary Outcomes (1)

• Accuracy of the intracolonoscopy AI-assisted optical diagnosis

  Using the optical diagnosis obtained from 2 experts as the reference, the accuracy of the intracolonoscopy AI-assisted optical diagnosis (OD, with endoscopist input or autonomous) is measured as the number of correct ODs out of all ODs (%).

  120 days

Secondary Outcomes (6)

• Proportion of patients who accept study participation

  120 days

• Proportion of the patients unwilling to participate due to concerns regarding undergoing an optical diagnosis

  120 days

• Agreement between the intracolonoscopy AI-assisted optical diagnosis and the AI-assisted optical diagnosis by experts

  120 days

• Diagnostic characteristics of AI-assisted optical diagnosis using adjudication by two expert endoscopists as the reference standard

  120 days

• Proportion of polyps with a low-confidence diagnosis

  120 days

Study Arms (2)

AI-assisted classification with endoscopist's input

OTHER

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.

Device: Artificial intelligence-assisted classification (CADx)

Autonomous AI-assisted classification

OTHER

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.

Device: Artificial intelligence-assisted classification (CADx)

Interventions

Artificial intelligence-assisted classification (CADx) DEVICE

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.

Also known as: CAD-eye

AI-assisted classification with endoscopist's input; Autonomous AI-assisted classification

Eligibility Criteria

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

You may qualify if:

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

You may not qualify if:

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

Sponsors & Collaborators

• Daniel Von Renteln: lead

Study Sites (1)

Centre Hospitalier de l'Université de Montréal

Montreal, Quebec, Canada

RECRUITING

Study Officials

• Daniel von Renteln, MD

  Centre hospitalier de l'Université de Montréal (CHUM)

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NON RANDOMIZED
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Gastroenterologist, MD

Study Record Dates

• First Submitted: September 22, 2023
• First Posted: September 28, 2023
• Study Start: September 1, 2023
• Primary Completion: May 1, 2025
• Study Completion: June 30, 2025
• Last Updated: February 19, 2025

Record last verified: 2025-02

Data Sharing

• IPD Sharing: Will not share

Locations

CA (1)