Artificial Intelligence System for the Detection and Prediction of Kidney Diseases Using Ocular Information

Study Overview
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Brief Summary
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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.

Official Title
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Artificial Intelligence System for the Detection and Prediction of Kidney Diseases Using Ocular Information

Conditions
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Artificial Intelligence, Ophthalmology, Kidney Diseases

Intervention / Treatment
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* Other: Diagnostic Test: Chronic Kidney Diseases


Participation Criteria
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Eligibility Criteria
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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.

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Development Dataset 01<br>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 | Other: Diagnostic Test: Chronic Kidney Diseases<br>* The development datasets were used to train the deep learning model, which was validated and tested by the other 4 datasets.<br>|
| Development Dataset 02<br>Slit-lamp, retinal fundus images, OCTA and kidney diseases examinations collected from Medical Centre of Aikang Health Care, Guangzhou, China | Other: Diagnostic Test: Chronic Kidney Diseases<br>* The development datasets were used to train the deep learning model, which was validated and tested by the other 4 datasets.<br>|
| Validation Dataset 01<br>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 | Other: Diagnostic Test: Chronic Kidney Diseases<br>* The development datasets were used to train the deep learning model, which was validated and tested by the other 4 datasets.<br>|
| Validation Dataset 02<br>Slit-lamp, retinal fundus images, OCTA and kidney diseases examinations collected from Medical Centre of Aikang Health Care, Guangzhou, China | Other: Diagnostic Test: Chronic Kidney Diseases<br>* The development datasets were used to train the deep learning model, which was validated and tested by the other 4 datasets.<br>|
| Test Dataset 01<br>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 | Other: Diagnostic Test: Chronic Kidney Diseases<br>* The development datasets were used to train the deep learning model, which was validated and tested by the other 4 datasets.<br>|
| Test Dataset 02<br>Slit-lamp, retinal fundus images, OCTA and kidney diseases examinations collected from Medical Centre of Aikang Health Care, Guangzhou, China | Other: Diagnostic Test: Chronic Kidney Diseases<br>* The development datasets were used to train the deep learning model, which was validated and tested by the other 4 datasets.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | 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 | 

Secondary Outcome Measures

| Outcome Measure | 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 | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Kidney Diseases, Artificial Intelligence, Eye information