Autonomous Artificial Intelligence Versus AI Assisted Human Optical Diagnosis

NCT ID: NCT06543862

Last Updated: 2024-11-15

Basic Information

• Recruitment Status: NOT_YET_RECRUITING
• Clinical Phase: NA
• Total Enrollment: 540 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2024-11-15
• Study Completion Date: 2024-11-15

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.

Detailed Description

Our study hypothesis is that for CADx implementation, instead of using the high/low confidence framework, identifying cases with suboptimal diagnostic accuracy could be facilitated through identifying cases in which CADx and endoscopist disagreed in their diagnosis. Eliminating such cases might separate out cases with low accuracy when using CADx assisted OD. Since endoscopists have a high sensitivity but low specificity for serrated polyp OD, this framework will also allow us to implement a strategy to adequately manage serrated polyps found in the cohort.

Conditions

Colonic Polyp; Artificial Intelligence

Study Design

• Allocation Method: NA
• Intervention Model: SINGLE_GROUP
• Primary Study Purpose: DIAGNOSTIC
• Blinding Strategy: NONE

Study Groups

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.

Group Type: OTHER

CADx (AI) system

Intervention Type: OTHER

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

Interventions

CADx (AI) system

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

Intervention Type: OTHER

Eligibility Criteria

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

• Minimum Eligible Age: 45 Years
• Maximum Eligible Age: 80 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Centre hospitalier de l'Université de Montréal (CHUM)

OTHER

Sponsor Role: lead

Responsible Party

Daniel Von Renteln

Principal Investigator

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Daniel von Renteln, MD

Role: PRINCIPAL_INVESTIGATOR

University of Montreal Medical Center (CHUM)

Locations

Ghislaine Ahoua

Montreal, Quebec, Canada

Countries

Canada

Central Contacts

Daniel von Renteln, MD

Role: CONTACT

daniel.von.renteln.med@ssss.gouv.qc.ca

514 890-8000 ext. 30912

Facility Contacts

Daniel von Renteln, MD

Role: primary

Other Identifiers

2025-12306

Identifier Type: -

Identifier Source: org_study_id