UMIN-CTR Clinical Trial
| Unique ID issued by UMIN | UMIN000057903 |
|---|---|
| Receipt number | R000066189 |
| Scientific Title | Systematic Review and Meta-analysis of Diagnostic Accuracy of Deep Learning Using Ultra-Widefield Fundus Imaging for Retinal Detachment |
| Date of disclosure of the study information | 2025/05/30 |
| Last modified on | 2025/05/24 11:43:18 |
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Basic information
Public title
Meta-analysis of Diagnostic Accuracy of Deep Learning Based on Ultra-Widefield Fundus Imaging for Retinal Detachment
Acronym
DL-UWF Meta-analysis for Retinal Detachment Diagnosis
Scientific Title
Systematic Review and Meta-analysis of Diagnostic Accuracy of Deep Learning Using Ultra-Widefield Fundus Imaging for Retinal Detachment
Scientific Title:Acronym
Systematic Review of DL*UWF for RD Diagnosis
Region
| Japan |
Condition
Condition
Retinal Detachment
Classification by specialty
| Ophthalmology |
Classification by malignancy
Others
Genomic information
NO
Objectives
Narrative objectives1
To systematically collect and synthesize existing studies on the automated diagnosis of retinal detachment using deep learning applied to ultra-widefield fundus imaging, and to evaluate diagnostic performance metrics such as sensitivity, specificity, and area under the curve (AUC).
Basic objectives2
Others
Basic objectives -Others
To evaluate the diagnostic performance of deep learning algorithms for retinal detachment detection, thereby providing a basis for discussing their potential applicability and future clinical implementation in ophthalmology.
Trial characteristics_1
Trial characteristics_2
Developmental phase
Assessment
Primary outcomes
Sensitivity and specificity of deep learning algorithms using ultra-widefield fundus images for the diagnosis of retinal detachment, based on the assessment at the time of diagnosis in each included study
Key secondary outcomes
Key secondary outcomes include the area under the summary receiver operating characteristic curve (AUC) at the time of diagnosis, the diagnostic odds ratio (DOR), between-study heterogeneity assessed by the I^2 statistic, and risk of bias evaluated using the QUADAS-2 tool.
Base
Study type
Others,meta-analysis etc
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
| Not applicable |
Age-upper limit
| Not applicable |
Gender
Male and Female
Key inclusion criteria
This meta-analysis includes studies that evaluate the diagnostic accuracy - specifically sensitivity and specificity - of deep learning algorithms using ultra-widefield fundus images for the detection of retinal detachment. Eligible study designs include case-control, cross-sectional, prospective, and retrospective observational studies. Studies must report sufficient data to allow calculation of diagnostic accuracy metrics (true positives, false positives, false negatives, true negatives). While peer-reviewed original articles are primarily targeted, conference abstracts may also be included if they contain adequate diagnostic information.
Key exclusion criteria
Studies that report only sensitivity or specificity, or that lack sufficient data to calculate diagnostic accuracy metrics, will be excluded. In addition, studies suspected of using duplicate datasets and non-original articles such as case reports, reviews, and editorials will also be excluded.
Target sample size
Research contact person
Name of lead principal investigator
| 1st name | Yuki |
| Middle name | |
| Last name | Mizuki |
Organization
Yokohama City University Hospital
Division name
Department of Ophthalmology
Zip code
236-0004
Address
3-9 Fukuura, Kanazawa-ku, Yokohama, Kanagawa 236-0004, Japan
TEL
0457872683
mizuki.yuk.xj@yokohama-cu.ac.jp
Public contact
Name of contact person
| 1st name | Yuki |
| Middle name | |
| Last name | Mizuki |
Organization
Yokohama City University Hospital
Division name
Department of Ophthalmology
Zip code
236-0004
Address
3-9 Fukuura, Kanazawa-ku, Yokohama, Kanagawa 236-0004, Japan
TEL
0457872683
Homepage URL
mizuki.yuk.xj@yokohama-cu.ac.jp
Sponsor or person
Institute
Yokohama City University
Institute
Department
Personal name
Yuki Mizuki
Funding Source
Organization
Yokohama City University
Organization
Division
Category of Funding Organization
Self funding
Nationality of Funding Organization
Other related organizations
Co-sponsor
Name of secondary funder(s)
IRB Contact (For public release)
Organization
Yokohama City University School of Medicine Department of Ophthalmology and Visual Science
Address
3-9 Fukuura, Kanazawa-ku, Yokohama, Kanagawa 236-0004, Japan
Tel
0457872683
mizuki.yuk.xj@yokohama-cu.ac.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
Kanagawa
Institutions
Institutions
Other administrative information
Date of disclosure of the study information
| 2025 | Year | 05 | Month | 30 | Day |
Related information
URL releasing protocol
Publication of results
Unpublished
Result
URL related to results and publications
Number of participants that the trial has enrolled
Results
Results date posted
Results Delayed
Results Delay Reason
Date of the first journal publication of results
Baseline Characteristics
Participant flow
Adverse events
Outcome measures
Plan to share IPD
IPD sharing Plan description
Progress
Recruitment status
Preinitiation
Date of protocol fixation
| 2025 | Year | 05 | Month | 19 | Day |
Date of IRB
Anticipated trial start date
| 2025 | Year | 05 | Month | 24 | Day |
Last follow-up date
| 2027 | Year | 03 | Month | 31 | Day |
Date of closure to data entry
Date trial data considered complete
Date analysis concluded
Other
Other related information
This study will be conducted in accordance with the PRISMA-DTA guidelines and the Cochrane Handbook, and is planned to be registered in both the UMIN Clinical Trials Registry and PROSPERO. The findings are expected to serve as foundational evidence for evaluating the potential implementation of artificial intelligence technologies in ophthalmic practice.
Management information
Registered date
| 2025 | Year | 05 | Month | 19 | Day |
Last modified on
| 2025 | Year | 05 | Month | 24 | Day |
Link to view the page
Value
https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000066189
