UMIN-CTR Clinical Trial
| Unique ID issued by UMIN | UMIN000040321 |
|---|---|
| Receipt number | R000046008 |
| Scientific Title | Development of Diagnostic Artificial intelligence in the Ophthalmology |
| Date of disclosure of the study information | 2020/05/07 |
| Last modified on | 2023/05/18 16:33:17 |
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Basic information
Public title
Development of Diagnostic Artificial intelligence in the Ophthalmology
Acronym
Development of Diagnostic Artificial intelligence in the Ophthalmology
Scientific Title
Development of Diagnostic Artificial intelligence in the Ophthalmology
Scientific Title:Acronym
Development of Diagnostic Artificial intelligence in the Ophthalmology
Region
| Japan |
Condition
Condition
Cataract, Dry Eye Disease, Keratoconus, Glaucoma, Allergic conjunctivitis, etc
Classification by specialty
| Ophthalmology |
Classification by malignancy
Others
Genomic information
NO
Objectives
Narrative objectives1
Trial of the Ophthalmological diagnostic AI
Basic objectives2
Efficacy
Basic objectives -Others
Trial characteristics_1
Trial characteristics_2
Developmental phase
Assessment
Primary outcomes
Clinical parameters by the Ophthalmological images
Key secondary outcomes
Base
Study type
Observational
Study design
Basic design
Randomization
Randomization unit
Blinding
Control
Stratification
Dynamic allocation
Institution consideration
Blocking
Concealment
Intervention
No. of arms
Purpose of intervention
Type of intervention
Interventions/Control_1
Interventions/Control_2
Interventions/Control_3
Interventions/Control_4
Interventions/Control_5
Interventions/Control_6
Interventions/Control_7
Interventions/Control_8
Interventions/Control_9
Interventions/Control_10
Eligibility
Age-lower limit
| 20 | years-old | <= |
Age-upper limit
| 99 | years-old | > |
Gender
Male and Female
Key inclusion criteria
Ophthalmological data in Keio University (IRB number:20090277,20170306,20180206) and Tsurumi University (IRB number:1634)
Key exclusion criteria
A case which does not want to commit to the clinical trial, no opt-in paper, other
Target sample size
300
Research contact person
Name of lead principal investigator
| 1st name | Shimizu |
| Middle name | |
| Last name | Eisuke |
Organization
Keio University School of Medicine
Division name
Department of Ophthalmology
Zip code
160-8582
Address
35 Shinanomachi, Shinjuku-ku
TEL
0353633972
ophthalmolog1st.acek39@keio.jp
Public contact
Name of contact person
| 1st name | Shimizu |
| Middle name | |
| Last name | Eisuke |
Organization
Keio University School of Medicine
Division name
Department of Ophthalmology
Zip code
160-8582
Address
35 Shinanomachi, Shinjuku-ku
TEL
0353633972
Homepage URL
ophthalmolog1st.acek39@keio.jp
Sponsor or person
Institute
Department of Ophthalmology, Keio University School of Medicine
Institute
Department
Personal name
Funding Source
Organization
Kakenhi
Organization
Division
Category of Funding Organization
Japanese Governmental office
Nationality of Funding Organization
Other related organizations
Co-sponsor
Name of secondary funder(s)
IRB Contact (For public release)
Organization
Keio University School of Medicine
Address
35 Shinanomachi, Shinjuku-ku
Tel
0333531211
ophthalmolog1st.acek39@keio.jp
Secondary IDs
Secondary IDs
NO
Study ID_1
Org. issuing International ID_1
Study ID_2
Org. issuing International ID_2
IND to MHLW
Institutions
Institutions
Other administrative information
Date of disclosure of the study information
| 2020 | Year | 05 | Month | 07 | Day |
Related information
URL releasing protocol
https://www.ctr.hosp.keio.ac.jp/news/003488.html
Publication of results
Partially published
Result
URL related to results and publications
https://www.ctr.hosp.keio.ac.jp/news/003488.html
Number of participants that the trial has enrolled
300
Results
The accuracy of tear film breakup time estimation was 0.789 (95% confidence interval (CI) 0.769 0.809), and the area under the receiver operating characteristic curve of this AI model was 0.877 (95% CI 0.861 0.893). The sensitivity and specificity of this AI model for the diagnosis of DED were 0.778 (95% CI 0.572 0.912) and 0.857 (95% CI 0.564 0.866), respectively. We successfully developed a novel AI-based diagnostic model for DED.
Results date posted
| 2023 | Year | 05 | Month | 18 | Day |
Results Delayed
Results Delay Reason
Date of the first journal publication of results
Baseline Characteristics
Japanese
Participant flow
Principal Investigator/Co-Principal Investigator selects the most appropriate case
Adverse events
none
Outcome measures
accuracy
Plan to share IPD
IPD sharing Plan description
Progress
Recruitment status
No longer recruiting
Date of protocol fixation
| 2020 | Year | 04 | Month | 01 | Day |
Date of IRB
| 2020 | Year | 05 | Month | 01 | Day |
Anticipated trial start date
| 2020 | Year | 05 | Month | 01 | Day |
Last follow-up date
| 2021 | Year | 03 | Month | 31 | Day |
Date of closure to data entry
Date trial data considered complete
Date analysis concluded
Other
Other related information
none
Management information
Registered date
| 2020 | Year | 05 | Month | 07 | Day |
Last modified on
| 2023 | Year | 05 | Month | 18 | Day |
Link to view the page
Value
https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000046008
