Artificial Intelligence System for the Detection and Prediction of Kidney Diseases Using Ocular Information
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, Ph. D
- Phone Number: 13802793086
- Email: gddlht@aliyun.com
Study Locations
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Guangdong
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Guangzhou, Guangdong, China, 510060
- Recruiting
- Zhongshan Ophthalmic Center, Sun Yat-sen University
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Contact:
- Haotian Lin, M.D., Ph.D
- Phone Number: +8613802793086
- Email: haot.lin@hotmail.com
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Contact:
- Qianni Wu, M.D., Ph.D
- Phone Number: +8615521506995
- Email: wuqianni@gzzoc.com
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-
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Genders Eligible for Study
Sampling Method
Study Population
Description
Inclusion Criteria:
- Patients previously received kidney biopsy, ophthalmic examinations and routine examinations of the department of nephrology during in-hospital period with BCVA>0.5.
Exclusion Criteria:
- Patients without retinal fundus images or kidney diseases.
- The quality of the retinal fundus images can not meet the requirement for furthur analysis.
- Severe loss of results of routine examinations of the department of nephrology.
Study Plan
How is the study designed?
Design Details
Number of groups / cohorts
Cohorts and Interventions
Group / CohortGroup / Cohort |
Intervention / TreatmentIntervention / Treatment |
|---|---|
|
Development Dataset 01
Slit-lamp, retinal fundus images, OCTA and kidney diseases examinations collected from Department of Nephrology of the First Affiliated Hospital of Sun Yat-sen University
|
The development datasets were used to train the deep learning model, which was validated and tested by the other 4 datasets.
|
|
Development Dataset 02
Slit-lamp, retinal fundus images, OCTA and kidney diseases examinations collected from Medical Centre of Aikang Health Care, Guangzhou, China
|
The development datasets were used to train the deep learning model, which was validated and tested by the other 4 datasets.
|
|
Validation Dataset 01
Slit-lamp, retinal fundus images, OCTA and kidney diseases examinations collected from Department of Nephrology of the First Affiliated Hospital of Sun Yat-sen University
|
The development datasets were used to train the deep learning model, which was validated and tested by the other 4 datasets.
|
|
Validation Dataset 02
Slit-lamp, retinal fundus images, OCTA and kidney diseases examinations collected from Medical Centre of Aikang Health Care, Guangzhou, China
|
The development datasets were used to train the deep learning model, which was validated and tested by the other 4 datasets.
|
|
Test Dataset 01
Slit-lamp, retinal fundus images, OCTA and kidney diseases examinations collected from Department of Nephrology of the First Affiliated Hospital of Sun Yat-sen University
|
The development datasets were used to train the deep learning model, which was validated and tested by the other 4 datasets.
|
|
Test Dataset 02
Slit-lamp, retinal fundus images, OCTA and kidney diseases examinations collected from Medical Centre of Aikang Health Care, Guangzhou, China
|
The development datasets were used to train the deep learning model, which was validated and tested by the other 4 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 and specificity of the deep learning system
Time Frame: baseline
|
The investigators will calculate the sensitivity and specifity of deep learning system and compare this index between deep learning system and human doctors
|
baseline
|
Collaborators and Investigators
Sponsor
Sponsor
Collaborators
Collaborators
Investigators
Investigators
- Study Chair: Yizhi Liu, M.D., Ph.D., Zhongshan Ophthalmic Center, Sun Yat-sen University
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
- AIKD-2021
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
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