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Using AI-assisted Optical Polyp Diagnosis for Diminutive Colorectal Polyps

NCT06059378 · Status: RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 204

Last updated 2025-02-19

No results posted yet for this study

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

Interventions

DEVICE

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.

Sponsors & Collaborators

  • Daniel Von Renteln

    lead OTHER

Principal Investigators

  • Daniel von Renteln, MD · Centre hospitalier de l'Université de Montréal (CHUM)

Study Design

Allocation
NON_RANDOMIZED
Purpose
DIAGNOSTIC
Masking
NONE
Model
PARALLEL

Eligibility

Min Age
45 Years
Max Age
80 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2023-09-01
Primary Completion
2025-05-01
Completion
2025-06-30

Countries

  • Canada

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06059378 on ClinicalTrials.gov