Effect of the Computer Aided Diagnosis with Explainable Artificial Intelligence for Colon Polyp on Optical Diagnosis and Acceptance of Technology

NCT06617468 | Active Not Recruiting

• 1 other identifier: 2311-049-1482
• Study Type: interventional
• Target: 120
• Locations: 1 country
• Sites: 1

Brief Summary

The goal of this clinical trial is to learn if computer-aided diagnosis with deep learning and computer-aided diagnosis with explainable AI work to optical diagnosis performance and acceptance of technology in endoscopists. The main questions it aims to answer are: Do computer-aided diagnosis with deep learning and computer-aided diagnosis with explainable AI improve optical diagnosis performance in endoscopists? Does experience using deep learning-based computer-assisted diagnosis and explainable AI-based computer-assisted diagnosis improve endoscopists' acceptance of computer-aided diagnosis as a technology? Participants will: Conduct a survey on acceptance and use of technology about computer-aided diagnosis. Perform a test to estimate the pathologic diagnosis on 200 NBI still images without the aid of computer-aided diagnosis. More than 1 month later, perform a same test to estimate the pathologic diagnosis on 200 NBI still images with computer-aided diagnosis with deep learning or explainable AI. Conduct a survey on acceptance and use of technology about computer-aided diagnosis.

Conditions

Colon Polyp

Keywords

colon polyp; optical diagnosis; computer-aided diagnosis

Outcome Measures

Primary Outcomes (1)

• Accuracy of optical diagnosis

  The proportion of cases in which pathological results are consistent with endoscopic estimation of adenoma and hyperplastic polyp

  From baseline test to the follow up test (more than 1 month later from baseline test)

Secondary Outcomes (1)

• acceptance of computer-aided diagnosis as a technology

  From baseline test to the follow up test (more than 1 month later from baseline test)

Study Arms (2)

computer-aided diagnosis with explainable AI

EXPERIMENTAL

Perform a same test to estimate the pathologic diagnosis on 200 NBI still images with computer-aided diagnosis with explainable AI

Diagnostic Test: computer-aided diagnosis with explainable AI

computer-aided diagnosis with deep learning

ACTIVE COMPARATOR

Perform a same test to estimate the pathologic diagnosis on 200 NBI still images with computer-aided diagnosis with deep learning

Diagnostic Test: computer-aided diagnosis with deep learning

Interventions

computer-aided diagnosis with explainable AI DIAGNOSTIC_TEST

Perform a same test to estimate the pathologic diagnosis on 200 NBI still images with computer-aided diagnosis with explainable AI.

computer-aided diagnosis with explainable AI

computer-aided diagnosis with deep learning DIAGNOSTIC_TEST

Perform a same test to estimate the pathologic diagnosis on 200 NBI still images with computer-aided diagnosis with deep Iearning.

computer-aided diagnosis with deep learning

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)

You may qualify if:

• Endoscopists with colonoscopy experience

You may not qualify if:

• Who can not perform colonoscopy

Sponsors & Collaborators

• Seoul National University Hospital: lead

Study Sites (1)

Healthcare System Gangnam Center, Seoul National University Hospital

Seoul, 06236, South Korea

Location

MeSH Terms

Conditions

Colonic Polyps

Interventions

Diagnosis, Computer-Assisted; Deep Learning

Condition Hierarchy (Ancestors)

Intestinal Polyps; Polyps; Pathological Conditions, Anatomical; Pathological Conditions, Signs and Symptoms

Intervention Hierarchy (Ancestors)

Diagnosis; Machine Learning; Artificial Intelligence; Algorithms; Mathematical Concepts; Neural Networks, Computer

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: professor

Study Record Dates

• First Submitted: September 25, 2024
• First Posted: September 27, 2024
• Study Start: September 20, 2024
• Primary Completion: December 31, 2024
• Study Completion: December 31, 2024
• Last Updated: October 4, 2024

Record last verified: 2024-09

Data Sharing

• IPD Sharing: Will not share

Locations

KR (1)