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

• Conditions: Artificial Intelligence; Ophthalmology; Kidney Diseases

• Registration Number: NCT05223712
• Lead Sponsor: Sun Yat-sen University

Brief 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.

Detailed Description

Not available

Study Timeline

• Study Start: 2021-08-28
• Status Verified: 2022-01
• Study First Submitted: 2022-01-23
• Study First Submitted QC: 2022-01-25
• Last Update Submitted: 2022-01-25
• Study First Posted: 2022-02-04
• Last Update Posted: 2022-02-04
• Primary Completion: 2022-12
• Study Completion: 2022-12

Recruitment & Eligibility

• Status: UNKNOWN
• Sex: All
• Target Recruitment: 4000

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 & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Area under the receiver operating characteristic curve of the deep learning system	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

Secondary Outcome Measures

Name	Time	Method
Sensitivity and specificity of the deep learning system	baseline	The investigators will calculate the sensitivity and specifity of deep learning system and compare this index between deep learning system and human doctors

Trial Locations

Locations (1)

Zhongshan Ophthalmic Center, Sun Yat-sen University; 🇨🇳 Guangzhou, Guangdong, China