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

January 25, 2022 updated by: Haotian Lin, Sun Yat-sen University
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.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Study Type

Observational

Enrollment (Anticipated)

4000

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

  • China
    • Guangdong
      • Guangzhou, Guangdong, China, 510060
        • Recruiting
        • Zhongshan Ophthalmic Center, Sun Yat-sen University
        • Contact:
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

18 years and older (Adult, Older Adult)

Accepts Healthy Volunteers

Yes

Genders Eligible for Study

All

Sampling Method

Probability Sample

Study Population

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

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

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / 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
Other: Diagnostic Test: Chronic Kidney Diseases
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
Other: Diagnostic Test: Chronic Kidney Diseases
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
Other: Diagnostic Test: Chronic Kidney Diseases
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
Other: Diagnostic Test: Chronic Kidney Diseases
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
Other: Diagnostic Test: Chronic Kidney Diseases
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
Other: Diagnostic Test: Chronic Kidney Diseases
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

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

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

This is where you will find people and organizations involved with this study.

Sponsor

Sun Yat-sen University

Collaborators

First Affiliated Hospital, Sun Yat-Sen University

Investigators

  • Study Chair: Yizhi Liu, M.D., Ph.D., Zhongshan Ophthalmic Center, Sun Yat-sen University

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

August 28, 2021

Primary Completion (Anticipated)

December 1, 2022

Study Completion (Anticipated)

December 1, 2022

Study Registration Dates

First Submitted

January 23, 2022

First Submitted That Met QC Criteria

January 25, 2022

First Posted (Actual)

February 4, 2022

Study Record Updates

Last Update Posted (Actual)

February 4, 2022

Last Update Submitted That Met QC Criteria

January 25, 2022

Last Verified

January 1, 2022

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • AIKD-2021

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

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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