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RECRUITING
NCT06059378
NA

Using AI-assisted Optical Polyp Diagnosis for Diminutive Colorectal Polyps

Sponsor: Daniel Von Renteln

View on ClinicalTrials.gov

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.

Official title: Using Artificial Intelligence-assisted Optical Polyp Diagnosis for Diminutive Colorectal Polyps

Key Details

Gender

All

Age Range

45 Years - 80 Years

Study Type

INTERVENTIONAL

Enrollment

204

Start Date

2023-09-01

Completion Date

2025-06-30

Last Updated

2025-02-19

Healthy Volunteers

No

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.

Locations (1)

Centre Hospitalier de l'Université de Montréal

Montreal, Quebec, Canada