Diagnostic Performance of Deep Learning Using Retinal Images for Detection of Chronic Kidney Disease: A Systematic Review and Meta-Analysis
Year: 2026
Type: review
Study designs: Systematic Review, Meta-Analysis
Abstract: INTRODUCTION: Multiple investigations have been conducted to diagnose chronic kidney disease (CKD) from photographs of retina using deep learning models, with a wide range of diagnostic performance, which necessitates the importance of a comprehensive review. Therefore, in this meta-analysis, we estimated the detection performance of deep learning models for the diagnosis of CKD using retinal photographs. METHODS: MEDLINE, Scopus, and Web of Science were searched until 25 December 2025. Pooled estimation of heterogeneity, diagnostic parameters, and meta-regressions was performed using MetaDisc and STATA Software packages. RESULTS: The pooled sensitivity, specificity, and diagnostic odds ratio were 0.81 (95% CI, 0.80-0.82), 0.64 (95% CI, 0.64-0.65), and 9.15 (95% CI, 5.72-14.62), respectively. The pooled estimates of the positive and negative likelihood ratios were 2.18 (95% CI, 1.66-2.87) and 0.28 (95% CI, 0.20-0.39), respectively. The area under the summary receiver operating characteristic curve showed a discriminative performance of 0.77. CONCLUSION: Deep learning based on retinal images had moderate diagnostic performance for detection of CKD. Relatively high sensitivity and modest specificity suggest that deep learning is more effective for ruling out CKD than for confirming the diagnosis that deep learning is more effective for ruling out CKD than for confirming the diagnosis. Deep learning-based retinal analysis may therefore serve as a supportive, noninvasive screening or triage tool but should not replace established diagnostic methods for CKD.
Source: Ophthalmic Research
DOI: 10.1159/ore/aedag003
Institutions: Niroo Research Institute, Shahid Beheshti University of Medical Sciences, University of Warwick, Yasuj University of Medical Sciences, High Institute for Education and Research in Transfusion Medicine, Capital Medical University, Hamedan University of Medical Sciences, University of Illinois Chicago
Language: English
Topic: Retinal Imaging and Analysis
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W7204442788