Colon PolypNCT06617468
Effect of the Computer Aided Diagnosis with Explainable Artificial Intelligence for Colon Polyp on Optical Diagnosis and Acceptance of Technology
A study testing computer-aided diagnosis with explainable AI and computer-aided diagnosis with deep learning in people with Colon Polyp, run by Seoul National University Hospital at 1 site in South Korea.
Trial 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. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Colon Polyp
- What is being tested?
- computer-aided diagnosis with explainable AI and computer-aided diagnosis with deep learning
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 120 participants
- Where
- 1 siteSouth Korea
- Study dates
- 20 Sep 2024to 31 Dec 2024
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Who may be able to join
- Endoscopists with colonoscopy experience
Who cannot join
- Who can not perform colonoscopy
Trial locations and contacts
1 site in 1 country.
South Korea1 site
Healthcare System Gangnam Center, Seoul National University Hospital
Seoul, 06236
About this study
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?
Read more
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
- Being tested
- computer-aided diagnosis with explainable AI, computer-aided diagnosis with deep learning
- Keywords
- colon polyp, optical diagnosis, computer-aided diagnosis
- Medical subject headings
- Colonic Polyps
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
computer-aided diagnosis with explainable AIDiagnostic 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 deep learningDiagnostic test
Perform a same test to estimate the pathologic diagnosis on 200 NBI still images with computer-aided diagnosis with deep Iearning.
| Study group | Role | What they receive |
|---|---|---|
| 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 Receives: 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 Receives: Diagnostic Test: computer-aided diagnosis with deep learning |
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy of optical diagnosis
The proportion of cases in which pathological results are consistent with endoscopic estimation of adenoma and hyperplastic polyp
Measured: 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
Survey on acceptance and use of technology about computer-aided diagnosis.
Measured: From baseline test to the follow up test (more than 1 month later from baseline test)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted27 Sep 2024
- Study start20 Sep 2024
- Primary completion31 Dec 2024 (estimated)
- Study completion31 Dec 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Colon PolypStudies of the same condition, open and under way.
- More trials run by Seoul National University HospitalThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Enrolment
- 120 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Seoul National University Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Su Jin Chung, Seoul National University Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06617468
- Sponsor's protocol ID
- 2311-049-1482
- Status last verified
- Sep 2024
- First submitted
- 25 Sep 2024
- Last updated
- 4 Oct 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 4 Oct 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Effect of the Computer Aided Diagnosis with Explainable Artificial Intelligence for Colon Polyp on Optical Diagnosis and Acceptance of Technology
- ClinicalTrials.gov ID
- NCT06617468
- Acronym / sponsor ID
- 2311-049-1482
- Conditions
- Colon Polyp
- Keywords
- colon polyp, optical diagnosis, computer-aided diagnosis
- Study type
- Interventional
- Recruitment status
- Unknown status
- Enrolment
- 120 participants (estimated)
- Lead sponsor
- Seoul National University HospitalUniversity, hospital or other
- Study start
- 20 Sep 2024 (actual)
- Primary completion
- 31 Dec 2024 (estimated)
- Study completion
- 31 Dec 2024 (estimated)
- First posted
- 27 Sep 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| computer-aided diagnosis with explainable AI | Experimental | Diagnostic Test: computer-aided diagnosis with explainable AIPerform 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 | Diagnostic Test: computer-aided diagnosis with deep learningPerform a same test to estimate the pathologic diagnosis on 200 NBI still images with computer-aided diagnosis with deep learning |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
Key inclusion criteria (1 listed)
- Endoscopists with colonoscopy experience
Key exclusion criteria (1 listed)
- Who can not perform colonoscopy
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| computer-aided diagnosis with explainable AIPerform 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 learningPerform a same test to estimate the pathologic diagnosis on 200 NBI still images with computer-aided diagnosis with deep Iearning. | Diagnostic test | computer-aided diagnosis with deep learning |
Primary outcome
Accuracy of optical diagnosis
Time frame: From baseline test to the follow up test (more than 1 month later from baseline test)
The proportion of cases in which pathological results are consistent with endoscopic estimation of adenoma and hyperplastic polyp
1 secondary outcome
acceptance of computer-aided diagnosis as a technology
Time frame: From baseline test to the follow up test (more than 1 month later from baseline test)
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 4 Oct 2024.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing computer-aided diagnosis with explainable AI and computer-aided diagnosis with deep learning in people with Colon Polyp, run by Seoul National University Hospital at 1 site in South Korea.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 4 Oct 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in South Korea. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy of optical diagnosis (time frame: From baseline test to the follow up test (more than 1 month later from baseline test)). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 4 Oct 2024.