Artificial intelligence-based quantification of retinal microvascular biomarkers from fundus photography of chronic kidney disease: a case-control study
By
Qiumei Gu
Min Liu
Weiwei Zhang
Zhengju Chen
Xingye Wang
Jie Wang
Ziyan He
Fang Lu
May 18, 2026
Clinical Scorecard: AI-Driven Analysis of Retinal Microvascular Indicators from Fundus Images in Chronic Kidney Disease: A Comparative Study
At a Glance
Category Detail
Condition Chronic Kidney Disease (CKD) Key Mechanisms Retinal microvascular alterations reflect systemic microvascular changes associated with CKD. Target Population Patients with CKD stages 1-5 and healthy controls. Care Setting Single-center case-control study.
Key Highlights
Lower arteriovenous ratio (AVR), reduced arterial tortuosity, and lower arterial vascular density are associated with CKD. Higher venous vascular density and vessel tortuosity within the 3PD region indicate positive associations with CKD. AI-derived retinal parameters demonstrate moderate discriminatory ability for CKD detection. Guideline-Based Recommendations
Diagnosis
Utilize AI-based analysis of fundus images to assess retinal microvascular parameters. Management
Consider retinal microvascular alterations as potential indicators for early CKD screening. Monitoring & Follow-up
Monitor retinal microvascular parameters in patients at risk for CKD. Risks
CKD is often asymptomatic in early stages, leading to underdiagnosis and increased morbidity. Patient & Prescribing Data
322 participants including 110 healthy controls and 212 CKD patients.
AI-derived retinal parameters may aid in non-invasive early detection of CKD.
Clinical Best Practices
Incorporate retinal imaging as a non-invasive tool for CKD risk identification. Adjust for confounding factors such as age, sex, and BMI in analyses. Related Resources & Content
