Journal of diabetes
Detection and Risk Prediction of Diabetes Related Kidney Disease Using Retinal Images: A Systematic Review and Meta-Analysis
By Otabir SAG, Tegegne TK, Nderitu P and 6 others.
Abstract
Aim
This study aimed to determine the effectiveness of deep learning models for detecting and predicting diabetes related kidney disease (DRKD) using two-dimensional retinal images.
Methods
A systematic review was conducted across eight databases from inception to July 14, 2026. Eligible cross-sectional and longitudinal studies assessed deep learning models for DRKD detection or prediction using retinal images alone, clinical data alone, or hybrid models. Data on study design, model development, validation, and performance metrics were extracted, with study quality appraised using QUADAS-2 and post hoc, PROBAST+AI. Results were synthesized narratively, with random effects meta analysis for hybrid models.
Results
Of 367 studies, six met inclusion criteria. Three cross-sectional studies of hybrid models contributed to the meta-analysis for DRKD detection, yielding pooled AUC 0.88, sensitivity 0.80, and specificity 0.81, though heterogeneity was high (I2 93.7%-99.8%). Three further studies were synthesized narratively rather than pooled: two cross-sectional studies used more severe or narrower CKD case definitions (AUC 0.86 and 0.96 respectively), and only one longitudinal study predicted the 5-year risk of DRKD (AUC 0.81, sensitivity 0.70, and specificity 0.77). External validation showed hybrid models consistently outperformed retinal-only (ΔAUC 0.03-0.11), but their advantage over clinical-only models was inconsistent.