Development and Validation of a Deep Learning System for Multiple Ocular Fundus Diseases Using Retinal Images
Development and Validation of a Deep Learning System for Multiple Ocular Fundus Diseases Using Retinal Images: a Multi-center Prospective Study
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Study Type
Study Type
Enrollment (Anticipated)
Enrollment
Contacts and Locations
Study Contact
Study Contact
- Name: Haotian Lin, PhD
- Phone Number: 13802793086
- Email: gddlht@aliyun.com
Study Locations
-
-
Guangdong
-
Guangzhou, Guangdong, China, 510060
- Recruiting
- Zhongshan Ophthalmic Center, Sun Yat-sen Univerisity
-
Contact:
- Haotian Lin, Ph.D
- Phone Number: +86-020-87330274
- Email: gddlht@aliyun.com
-
Principal Investigator:
- Haotian Lin, Ph.D
-
-
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
- Child
- Adult
- Older Adult
Accepts Healthy Volunteers
Genders Eligible for Study
Sampling Method
Study Population
Description
Inclusion Criteria:
- The quality of fundus images should clinical acceptable. More than 80% of the fundus image area including four main regions (optic disk, macular, upper and lower retinal vessel archs) are easy to read and discriminate.
Exclusion Criteria:
- Images with light leakage (>30% of area), spots from lens flares or stains, and overexposure were excluded from further analysis.
Study Plan
How is the study designed?
Design Details
- Observational Models: Other
- Time Perspectives: Other
Number of groups / cohorts
Cohorts and Interventions
Group / CohortGroup / Cohort |
Intervention / TreatmentIntervention / Treatment |
|---|---|
|
Training dataset
Retinal images collected from hospitals and multiple screening sites all over China
|
|
|
Validation dataset
Retinal images separated from training dataset
|
Training dataset was used to train the deep learning model, which was validated and tested by other two datasets.
|
|
Testing dataset
Retinal images prospectively collected from the hospitals and ocular disease screening sites totally different from training dataset
|
Training dataset was used to train the deep learning model, which was validated and tested by other two datasets.
|
What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Area under the receiver operating characteristic curve of the deep learning system
Time Frame: baseline
|
The investigators will calculate the area under the receiver operating characteristic curve of deep learning system and compare this index between deep learning system and human doctors.
|
baseline
|
Secondary Outcome Measures
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Sensitivity of the deep learning system
Time Frame: baseline
|
The investigators will calculate the sensitivity of deep learning system and compare this index between deep learning system and human doctors.
|
baseline
|
|
Specificity of the deep learning system
Time Frame: baseline
|
The investigators will calculate the specificity of deep learning system and compare this index between deep learning system and human doctors.
|
baseline
|
Collaborators and Investigators
Sponsor
Sponsor
Publications and helpful links
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Anticipated)
Primary Completion
Study Completion (Anticipated)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
Other Study ID Numbers
- CCPMOH2019- China8
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
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