Effect of the Computer Aided Diagnosis with Explainable Artificial Intelligence for Colon Polyp on Optical Diagnosis and Acceptance of Technology
NCT ID: NCT06617468
Last Updated: 2024-10-04
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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ACTIVE_NOT_RECRUITING
NA
120 participants
INTERVENTIONAL
2024-09-20
2024-12-31
Brief Summary
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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.
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Detailed Description
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Conditions
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Study Design
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RANDOMIZED
PARALLEL
DIAGNOSTIC
NONE
Study Groups
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computer-aided diagnosis with explainable AI
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
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 deep learning
Perform a same test to estimate the pathologic diagnosis on 200 NBI still images with computer-aided diagnosis with deep learning
computer-aided diagnosis with deep learning
Perform a same test to estimate the pathologic diagnosis on 200 NBI still images with computer-aided diagnosis with deep Iearning.
Interventions
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computer-aided diagnosis with explainable AI
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 deep learning
Perform a same test to estimate the pathologic diagnosis on 200 NBI still images with computer-aided diagnosis with deep Iearning.
Eligibility Criteria
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Inclusion Criteria
Exclusion Criteria
ALL
No
Sponsors
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Seoul National University Hospital
OTHER
Responsible Party
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Su Jin Chung
professor
Locations
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Healthcare System Gangnam Center, Seoul National University Hospital
Seoul, , South Korea
Countries
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Other Identifiers
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2311-049-1482
Identifier Type: -
Identifier Source: org_study_id
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