 A noninvasive model for chronic kidney disease screening and common pathological type identification from retinal imagesQianni Wu,
Jianbo Li,
Lanqin Zhao,
Dong Liu,
Jingyi Wen,
Yunuo Wang,
Yiqin Wang,
Naya Huang,
Lanping Jiang,
Qinghua Liu,
Hanming Lin,
Pengxia Wan,
Shicong Yang,
Wenfang Chen,
Hongjian Ye,
Mohammed Haji Rashid Hassan,
Ahmed Hassan Nur,
Zefang Dai,
Jie Guo,
Shanshan Zhou,
Jianwen Yu,
Weixing Zhang,
Wenben Chen,
Ruiyang Li,
Wai Cheng Iao,
Juan-juan Feng,
Yan Wang,
Hua Hong,
Peihong Yin,
Qing Ye,
Chao Xie,
Min Zhu,
Xiaoyi Liu,
Yaozhong Kong,
Jie Wang,
Ruiying Ma,
Yu Xiao,
Guoguang Chen,
Rongguo Fu,
Yuhe Ke,
Jasmine Ong Chiat Ling,
Charumathi Sabanayagam,
Daniel Shu Wei Ting,
Kar Keung Cheng,
Duoru Lin (),
Wei Chen () and
Haotian Lin ()
Nature Communications, 2025, vol. 16, issue 1, 1-15 Abstract: 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. Date: 2025 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-62273-0 Ordering information: This journal article can be ordered from DOI: 10.1038/s41467-025-62273-0 Access Statistics for this article Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie More articles in Nature Communications from Nature |
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