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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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| 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 |
|
| 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 |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| 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. |
| Measure | Description | Time Frame |
|---|---|---|
| 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) |
| Measure | Description | Time Frame |
|---|---|---|
| acceptance of computer-aided diagnosis as a technology | Survey on acceptance and use of technology about computer-aided diagnosis. | From baseline test to the follow up test (more than 1 month later from baseline test) |
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Inclusion Criteria:
Exclusion Criteria:
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Healthcare System Gangnam Center, Seoul National University Hospital | Seoul | 06236 | South Korea |
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| 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. |
|
| ID | Term |
|---|---|
| D003111 | Colonic Polyps |
| ID | Term |
|---|---|
| D007417 | Intestinal Polyps |
| D011127 | Polyps |
| D020763 | Pathological Conditions, Anatomical |
| D013568 | Pathological Conditions, Signs and Symptoms |
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| ID | Term |
|---|---|
| D003936 | Diagnosis, Computer-Assisted |
| D000077321 | Deep Learning |
| ID | Term |
|---|---|
| D003933 | Diagnosis |
| D000069550 | Machine Learning |
| D001185 | Artificial Intelligence |
| D000465 | Algorithms |
| D055641 | Mathematical Concepts |
| D016571 | Neural Networks, Computer |
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