A noninvasive model for chronic kidney disease screening and common pathological type identification from retinal images

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 predict...

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Main Authors: Qianni 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, Haotian Lin
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-025-62273-0
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author Qianni 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
Haotian Lin
author_facet Qianni 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
Haotian Lin
author_sort Qianni Wu
collection DOAJ
description 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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spelling doaj-art-b148bb92f3ce498ea9b48f6ad96f17772025-08-20T03:46:12ZengNature PortfolioNature Communications2041-17232025-07-0116111510.1038/s41467-025-62273-0A noninvasive model for chronic kidney disease screening and common pathological type identification from retinal imagesQianni Wu0Jianbo Li1Lanqin Zhao2Dong Liu3Jingyi Wen4Yunuo Wang5Yiqin Wang6Naya Huang7Lanping Jiang8Qinghua Liu9Hanming Lin10Pengxia Wan11Shicong Yang12Wenfang Chen13Hongjian Ye14Mohammed Haji Rashid Hassan15Ahmed Hassan Nur16Zefang Dai17Jie Guo18Shanshan Zhou19Jianwen Yu20Weixing Zhang21Wenben Chen22Ruiyang Li23Wai Cheng Iao24Juan-juan Feng25Yan Wang26Hua Hong27Peihong Yin28Qing Ye29Chao Xie30Min Zhu31Xiaoyi Liu32Yaozhong Kong33Jie Wang34Ruiying Ma35Yu Xiao36Guoguang Chen37Rongguo Fu38Yuhe Ke39Jasmine Ong Chiat Ling40Charumathi Sabanayagam41Daniel Shu Wei Ting42Kar Keung Cheng43Duoru Lin44Wei Chen45Haotian Lin46State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular DiseasesDepartment of Nephrology, The First Affiliated Hospital, Sun Yat-sen UniversityState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular DiseasesState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular DiseasesState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular DiseasesDepartment of Nephrology, The First Affiliated Hospital, Sun Yat-sen UniversityDepartment of Nephrology, The First Affiliated Hospital, Sun Yat-sen UniversityDepartment of Nephrology, The First Affiliated Hospital, Sun Yat-sen UniversityDepartment of Nephrology, The First Affiliated Hospital, Sun Yat-sen UniversityDepartment of Nephrology, The First Affiliated Hospital, Sun Yat-sen UniversityDepartment of Ophthalmology, The First Affiliated Hospital, Sun Yat-sen UniversityDepartment of Ophthalmology, The First Affiliated Hospital, Sun Yat-sen UniversityDepartment of Pathology, The First Affiliated Hospital, Sun Yat-sen UniversityDepartment of Pathology, The First Affiliated Hospital, Sun Yat-sen UniversityDepartment of Nephrology, The First Affiliated Hospital, Sun Yat-sen UniversityBanadir HospitalBanadir HospitalDepartment of Nephrology, The First Affiliated Hospital, Sun Yat-sen UniversityDepartment of Nephrology, The First People’s Hospital of KashiDepartment of Nephrology, The First People’s Hospital of KashiDepartment of Nephrology, The First Affiliated Hospital, Sun Yat-sen UniversityState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular DiseasesState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular DiseasesState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular DiseasesState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular DiseasesHealth Management Center, The First Affiliated Hospital, Sun Yat-sen UniversityHealth Management Center, The First Affiliated Hospital, Sun Yat-sen UniversityHealth Management Center, The First Affiliated Hospital, Sun Yat-sen UniversityDepartment of Nephrology, Zhongshan City People’s HospitalDepartment of Nephrology, Zhongshan City People’s HospitalThe First People’s Hospital of FoshanThe First People’s Hospital of FoshanThe First People’s Hospital of FoshanThe First People’s Hospital of FoshanDepartment of Nephrology, Affiliated Hospital of Youjiang Medical University for NationalitiesDepartment of Nephrology, Affiliated Hospital of Youjiang Medical University for NationalitiesDepartment of Nephrology, Affiliated Hospital of Youjiang Medical University for NationalitiesDepartment of Nephrology, Shanxi Provincial Traditional Chinese Medicine InstituteDepartment of Nephrology, Second Affiliated hospital of Xi’an Jiaotong UniversityDepartment of Anesthesiology, Singapore General HospitalDuke-NUS Medical SchoolOphthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical SchoolDuke-NUS Medical SchoolInstitute of Applied Health Research, University of BirminghamState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular DiseasesDepartment of Nephrology, The First Affiliated Hospital, Sun Yat-sen UniversityState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular DiseasesAbstract 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.https://doi.org/10.1038/s41467-025-62273-0
spellingShingle Qianni 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
Haotian Lin
A noninvasive model for chronic kidney disease screening and common pathological type identification from retinal images
Nature Communications
title A noninvasive model for chronic kidney disease screening and common pathological type identification from retinal images
title_full A noninvasive model for chronic kidney disease screening and common pathological type identification from retinal images
title_fullStr A noninvasive model for chronic kidney disease screening and common pathological type identification from retinal images
title_full_unstemmed A noninvasive model for chronic kidney disease screening and common pathological type identification from retinal images
title_short A noninvasive model for chronic kidney disease screening and common pathological type identification from retinal images
title_sort noninvasive model for chronic kidney disease screening and common pathological type identification from retinal images
url https://doi.org/10.1038/s41467-025-62273-0
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