ArticleNPJ digital medicine2024
Screening chronic kidney disease through deep learning utilizing ultra-wide-field fundus images.
Article in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Development of a multimodal large language model for early warning and diagnosis of chronic ocular GVHD.NPJ digital medicine · 2026Article
- Replication of the Association between Retinal Aging Clock Susceptibility Genes and Retinal Age Gap in an Asian Population: The Nagahama Study.Ophthalmology science · 2026Article
- A fundus image dataset for intelligent diabetic retinopathy system.Scientific data · 2026Article
- Risk stratification in diabetic kidney disease: a review of prediction models for methodological advances and clinical application.Journal of translational medicine · 2026Review
- Artificial intelligence-based quantification of retinal microvascular biomarkers from fundus photography of chronic kidney disease: a case-control study.Frontiers in medicine · 2026Article
- Effect of RAS inhibitors on retinal microvasculature in DKD using AI-based ultra-wide-field images.Frontiers in endocrinology · 2026Article
- Explainable Artificial Intelligence for Early Diabetic Kidney Disease Risk Profiling in Type 2 Diabetes: Translational Readiness and Clinical Boundaries.Diabetes, metabolic syndrome and obesity : targets and therapy · 2026Review
- Multimodal deep learning improving the accuracy of pathological diagnoses for membranous nephropathy.Renal failure · 2025Article
- Emerging Biomarkers and Advanced Diagnostics in Chronic Kidney Disease: Early Detection Through Multi-Omics and AI.Diagnostics (Basel, Switzerland) · 2025Review
- Noninvasive early prediction of preeclampsia in pregnancy using retinal vascular features.NPJ digital medicine · 2025Article
- Artificial intelligence approaches to enable early detection of CKD.Nature reviews. Nephrology · 2025Article
- Artificial intelligence in chronic kidney disease: Bibliometric and visual analysis of trends and future directions.Digital healthArticle
Corrections and comments
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Authors and funding
39 authors.
Funding
Abstract
To address challenges in screening for chronic kidney disease (CKD), we devised a deep learning-based CKD screening model named UWF-CKDS. It utilizes ultra-wide-field (UWF) fundus images to predict the presence of CKD. We validated the model with data from 23 tertiary hospitals across China. Retinal vessels and retinal microvascular parameters (RMPs) were extracted to enhance model interpretability, which revealed a significant correlation between renal function and RMPs. UWF-CKDS, utilizing UWF images, RMPs, and relevant medical history, can accurately determine CKD status. Importantly, UWF-CKDS exhibited superior performance compared to CTR-CKDS, a model developed using the central region (CTR) cropped from UWF images, underscoring the contribution of the peripheral retina in predicting renal function. The study presents UWF-CKDS as a highly implementable method for large-scale and accurate CKD screening at the population level.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.