ArticleNature communications2025
A noninvasive model for chronic kidney disease screening and common pathological type identification from retinal images.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 6 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- From fundus to filtration: AI-driven retinal phenotyping as a framework for non-invasive prediction of kidney pathological categories (the "virtual renal biopsy" concept)- a narrative review.International urology and nephrology · 2026Review
- Artificial intelligence in nephrology: predicting CKD progression and personalizing treatment.International urology and nephrology · 2026Review
- A multimodal feature disentanglement model for lymphadenopathy diagnosis based on BUS and CDFI ultrasound videos: a retrospective, prospective, multicenter study.European radiology · 2026Article
- The retina-body axis: proteomic mechanisms linking oculomics and clinical traits in a female aging cohort.npj aging · 2026Article
- DiFFA: A Diffusion-Based Model for Synthetic Fundus Fluorescein Angiography to Enhance Detection of Diabetic Microvascular Complication.Translational vision science & technology · 2026Observational
- Multi-omics and machine learning identify FN1 and ALDH2 as diagnostic biomarkers and therapeutic targets in early and late diabetic kidney disease.Renal failure · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
47 authors.
Funding
Abstract
Chronic kidney disease (CKD) is a global health challenge, but invasive renal biopsies, the gold standard for diagnosis and prognosis, are often clinically constrained. To address this, we developed the kidney intelligent diagnosis system (KIDS), a noninvasive model for renal biopsy prediction using 13,144 retinal images from 6773 participants. The KIDS achieves an area under the receiver operating characteristic curve (AUC) of 0.839-0.993 for CKD screening and accurately identifies the five most common pathological types (AUC: 0.790-0.932) in a multicenter and multi-ethnic validation, outperforming nephrologists by 26.98% in accuracy. Additionally, the KIDS further predicts disease progression based on pathological classification. Given its flexible strategy, the KIDS can be adapted to local conditions to provide a tailored tool for patients. This noninvasive model has the potential to improve CKD clinical management, particularly for those who are ineligible for biopsies.
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Registered trials
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