Deep-learning models for the detection and incidence prediction of chronic kidney disease and type 2 diabetes from retinal fundus images
Abstract
Regular screening for the early detection of common chronic diseases might benefit from the use of deep-learning approaches, particularly in resource-poor or remote settings. Here we show that deep-learning models can be used to identify chronic kidney disease and type 2 diabetes solely from fundus images or in combination with clinical metadata (age, sex, height, weight, body-mass index and blood pressure) with areas under the receiver operating characteristic curve of 0.85-0.93. The models were trained and validated with a total of 115,344 retinal fundus photographs from 57,672 patients and can also be used to predict estimated glomerulal filtration rates and blood-glucose levels, with mean absolute errors of 11.1-13.4 ml min -1 per 1.73 m 2 and 0.65-1.1 mmol l -1 , and to stratify patients according to disease-progression risk. We evaluated the generalizability of the models for the identification of chronic kidney disease and type 2 diabetes with population-based external validation cohorts and via a prospective study with fundus images captured with smartphones, and assessed the feasibility of predicting disease progression in a longitudinal cohort.
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Retinal images were used to predict the incidence and progression of chronic kidney disease and diabetic retinopathy.
“Recently, retinal images were used to predict the incidence and progression of chronic kidney disease (CKD)39 and diabetic retinopathy (DR).101,102”
From Artificial intelligence in diabetes management: Advancements, opportunities, and challenges (2023) · cites this paper as [39]
For CKD and diabetic nephropathy screening, studies utilized clinical data, metabolic biomarkers, genotyping data, and/or retinal images to achieve AUCs generally over 0.8.
“For CKD39,110,111 and diabetic nephropathy112,113 screening, studies utilized clinical data, metabolic biomarkers, genotyping data, and/or retinal images to achieve AUCs generally over 0.8.”
From Artificial intelligence in diabetes management: Advancements, opportunities, and challenges (2023) · cites this paper as [39]
Zhang et al. reported that a CNN using retinal images and risk factors for CKD screening could identify CKD solely from fundus images or in combination with clinical metadata with AUCs of 0.85-0.93.
“the system could be used to identify CKD solely from fundus images or in combination with clinical metadata with AUCs of 0.85–0.93”
From Artificial intelligence in diabetes management: Advancements, opportunities, and challenges (2023) · cites this paper as Zhang et al.39
Zhang et al. reported that a CNN using retinal images and risk factors to predict development of chronic kidney disease achieved a C index of 0.845 on the internal test set and 0.719 on the external test set.
“the combined model could achieve a C index of 0.845 on the internal test set and 0.719 on the external test set”
From Artificial intelligence in diabetes management: Advancements, opportunities, and challenges (2023) · cites this paper as Zhang et al.39
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- Authors
- Zhang, K., Liu, X., Xu, J., Yuan, J., Cai, W., Chen, T., Wang, K., Gao, Y., Nie, S., Xu, X., Qin, X., Su, Y., Xu, W., Olvera, A., Xue, K., Li, Z., Zhang, M., Zeng, X., Zhang, C. L., Li, O., Zhang, E. E., Zhu, J., Xu, Y., Kermany, D., Zhou, K., Pan, Y., Li, S., Lai, I. F., Chi, Y., Wang, C., Pei, M., Zang, G., Zhang, Q., Lau, J., Lam, D., Zou, X., Wumaier, A., Wang, J., Shen, Y., Hou, F. F., Zhang, P., Xu, T., Zhou, Y., & Wang, G.
- Affiliations
- Macau University of Science and Technology (MO)
- Tsinghua University (CN)
- Beijing Tongren Hospital (CN)
- Sun Yat-sen University (CN)
- Beijing University of Posts and Telecommunications (CN)
- Nanfang Hospital (CN)
- Sichuan University (CN)
- University of Oxford (GB)
- Guangzhou University (CN)
- Guangzhou Women and Children Medical Center (CN)
- Jiangsu University (CN)
- University of Chinese Academy of Sciences (CN)
- Kiang Wu Hospital (CN)
- Peking University (CN)
- Third Affiliated Hospital of Sun Yat-sen University (CN)
- Hong Kong Polytechnic University (HK)
- Wuhan University (CN)
- Shanghai Jiao Tong University (CN)
- Corresponding author
- Zhang, Kang
- Chen, Ting
- Xu, Tao
- Zhou, Yong
- Wang, Guangyu
- ORCID
- Zhang, Kang
- Liu, Xiaohong
- Xu, Jie
- Yuan, Jin
- Cai, Wenjia
- Chen, Ting
- Gao, Yuanxu
- Nie, Sheng
- Xu, Xiaodong
- Qin, Xiaoqi
- Su, Yuandong
- Xue, Kanmin
- Li, Zhihuan
- Zhang, Meixia
- Zeng, Xiaoxi
- Zhang, Charlotte L.
- Zhu, Jie
- Xu, Yiming
- Kermany, Daniel
- Zhou, Kaixin
- Pan, Ying
- Li, Shaoyun
- Chi, Ying
- Pei, Michelle
- Zhang, Qi
- Lam, Dennis
- Zou, Xiaoguang
- Wumaier, Aizezi
- Wang, Jianquan
- Shen, Yin
- Hou, Fan Fan
- Zhang, Ping
- Xu, Tao
- Zhou, Yong
- Wang, Guangyu
- Venue
- Nature Biomedical Engineering
- Published
- 2021-06-15
- Volume
- 5
- Issue
- 6
- Pages
- 533-545
- ISSN
- 2157-846X (ISSN-L)
- OpenAlex
- W3168947860
- Cited by
- 324
- References
- 9
- Citations by year
- 2026: 53 · 2025: 74 · 2024: 80 · 2023: 72 · 2022: 35 · 2021: 10
- FWCI
- 26.42
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Nature Biomedical Engineering (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Wellcome Trust (216593/Z/19/Z)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Retinal Imaging and Analysis
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Ophthalmology › Ophthalmology and Visual Health Research
- Keywords
- chronic kidney disease, type 2 diabetes, fundus images, deep learning, disease detection, incidence forecasting, estimated glomerular filtration rate, blood glucose prediction, disease progression risk stratification, clinical metadata, external validation, resource-limited settings
- MeSH terms
- Metadata, Deep Learning, Blood Glucose, Body Height, Body Weight, Diabetes Mellitus, Type 2, Female, Fundus Oculi, Glomerular Filtration Rate, Humans, Image Interpretation, Computer-Assisted, Male, Middle Aged, Photography, Prospective Studies, Retina, ROC Curve, Body Mass Index, Neural Networks, Computer, Disease Progression, Area Under Curve, Renal Insufficiency, Chronic
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