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

Kidney Diseases Clinical Trial

Official title:

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

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05223712
• Other study ID #: AIKD-2021
• Status: Recruiting
• Start date: August 28, 2021
• Est. completion date: December 2022

Study information

• Verified date: January 2022
• Source: Sun Yat-sen University
• Contact: Haotian Lin, Ph. D
• Phone: 13802793086
• Email: gddlht[@]aliyun.com
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 4000
• Est. completion date: December 2022
• Est. primary completion date: December 2022
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years and older

Eligibility

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.

Related Conditions & MeSH terms

• Artificial Intelligence
• Kidney Diseases
• Ophthalmology

Intervention

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.

Locations

Country	Name	City	State
China	Zhongshan Ophthalmic Center, Sun Yat-sen University	Guangzhou	Guangdong

Sponsors (2)

Lead Sponsor	Collaborator
Sun Yat-sen University	First Affiliated Hospital, Sun Yat-Sen University

Country where clinical trial is conducted

China, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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	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