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| Name | Class |
|---|---|
| First Affiliated Hospital, Sun Yat-Sen University | OTHER |
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This is an retrospective and prospective multicenter study to develop and validate an artificial intelligent (AI) aided diagnosis, therapeutic effect assessment model including chronic kidney disease (CKD) and dialysis patients starting from April 2009, which is based on ophthalmic examinations (e.g. retinal fundus photography, slit-lamp images, OCTA, etc.) and CKD diagnostic and therapeutic data (routine clinical evaluations and laboratory data), to provide a reliable basis and guideline for clinical diagnosis and treatment.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| 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 |
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| Development Dataset 02 | Slit-lamp, retinal fundus images, OCTA and kidney diseases examinations collected from Medical Centre of Aikang Health Care, Guangzhou, China |
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| 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 |
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| Validation Dataset 02 | Slit-lamp, retinal fundus images, OCTA and kidney diseases examinations collected from Medical Centre of Aikang Health Care, Guangzhou, China |
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| 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 |
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Diagnostic Test: Chronic Kidney Diseases | Other | The development datasets were used to train the deep learning model, which was validated and tested by the other 4 datasets. |
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| Measure | Description | Time Frame |
|---|---|---|
| Area under the receiver operating characteristic curve of the deep learning system | 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 |
| Measure | Description | Time Frame |
|---|---|---|
| Sensitivity and specificity of the deep learning system | The investigators will calculate the sensitivity and specifity of deep learning system and compare this index between deep learning system and human doctors | baseline |
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Inclusion Criteria:
Exclusion Criteria:
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Participants who had slit-lamp, retinal fundus photography and kidney disease tests at the Department of Nephrology, First Affiliated Hospital of Sun Yat-sen University and Medical Centre of Aikang Health Care, Guangzhou, China
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Haotian Lin, Ph. D | Contact | 13802793086 | gddlht@aliyun.com |
| Name | Affiliation | Role |
|---|---|---|
| Yizhi Liu, M.D., Ph.D. | Zhongshan Ophthalmic Center, Sun Yat-sen University | Study Chair |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Zhongshan Ophthalmic Center, Sun Yat-sen University | Recruiting | Guangzhou | Guangdong | 510060 | China |
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| ID | Term |
|---|---|
| D007674 | Kidney Diseases |
| ID | Term |
|---|---|
| D014570 | Urologic Diseases |
| D052776 | Female Urogenital Diseases |
| D005261 | Female Urogenital Diseases and Pregnancy Complications |
| D000091642 | Urogenital Diseases |
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Blood, urine and renal biopsy samples from CKD patients.
| Test Dataset 02 | Slit-lamp, retinal fundus images, OCTA and kidney diseases examinations collected from Medical Centre of Aikang Health Care, Guangzhou, China |
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| D052801 | Male Urogenital Diseases |