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Deep-learning models for the detection and incidence prediction of chronic kidney disease and type 2 diabetes from retinal fundus images

Nature Biomedical Engineering volume 5, pages 533–545 (2021)Cite this article

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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 m2 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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Fig. 1: AI system for the detection and incidence prediction of systemic diseases using retinal fundus images.
Fig. 2: Performance of the AI models in the identification of CKD and early CKD.
Fig. 3: Model performance in assessing eGFR from retinal fundus images.
Fig. 4: Kaplan–Meier plots for the prediction of CKD and advanced+ CKD development.
Fig. 5: Performance of the AI system in the identification and incidence prediction of T2DM.
Fig. 6: Gradient visualizations of AI predictions of CKD staging and T2DM using the integrated gradient algorithm.

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Data availability

Restrictions apply to the availability of the developmental and validation datasets, which were used with permission of the participants for the current study. De-identified data may be available for research purposes from the corresponding authors on reasonable request.

Code availability

The deep-learning models were developed and deployed using standard model libraries and the PyTorch framework. The models can be trained via the publicly available ResNet-50 architecture starting from the pretrained models, available at https://github.com/pytorch/vision. Custom codes were specific to our development environment and used primarily for data input/output and parallelization across computers and graphics processors. The codes may be available for research purposes from the corresponding authors on reasonable request.

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Acknowledgements

This study was funded by the National Natural Science Foundation of China (61906105, 61872218 and 61721003), National Key Research and Development Program of China (2019YFB1404804, 2017YFC1104600, 2017YFC0112402), 1.3.5 project for disciplines of excellence, West China Hospital, Sichuan University (ZYJC20001, ZYJC18010), Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Macau University of Science and Technology, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University Initiative Scientific Research Program, and Guoqiang Institute, Tsinghua University, Wellcome Trust (216593/Z/19/Z). We thank members of the Zhang, Yuan and Wang groups for their assistance. We thank many volunteers and physicians for grading retinal photographs.

Author information

Author notes

  1. These authors contributed equally: Kang Zhang, Xiaohong Liu, Jie Xu, Jin Yuan, Wenjia Cai.

Authors and Affiliations

  1. Center for Clinical Translational Innovations and Biomedical Big Data Center, West China Hospital and Sichuan University, Chengdu, China

    Kang Zhang, Yuandong Su, Wenqin Xu, Andrea Olvera, Zhihuan Li, Meixia Zhang, Xiaoxi Zeng & Daniel Kermany

  2. Center for Biomedicine and Innovations, Faculty of Medicine, Macau University of Science and Technology and University Hospital, Macau, China

    Kang Zhang, Yuanxu Gao, Michelle Pei & Guangxi Zang

  3. Department of Computer Science and Technology, Tsinghua University, Beijing, China

    Xiaohong Liu, Ting Chen & Yiming Xu

  4. Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Beijing Ophthalmology and Visual Science Key Lab, Beijing, China

    Jie Xu

  5. State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China

    Jie Xu, Kai Wang, Xiaodong Xu, Xiaoqi Qin, Ping Zhang & Guangyu Wang

  6. State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China

    Jin Yuan & Wenjia Cai

  7. State Key Laboratory of Organ Failure Research, National Clinical Research Center for Kidney Disease and Nanfang Hospital, Southern Medical University, Guangzhou, China

    Sheng Nie & Fan Fan Hou

  8. Nuffield Laboratory of Ophthalmology, Department of Clinical Neurosciences, University of Oxford and Oxford University Hospitals NHS Foundation Trust, Oxford, UK

    Kanmin Xue

  9. Kidney Research Institute, Nephrology Division, West China Hospital and Sichuan University, Chengdu, China

    Xiaoxi Zeng

  10. Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, China

    Charlotte L. Zhang, Oulan Li, Edward E. Zhang, Kaixin Zhou & Tao Xu

  11. Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou, China

    Jie Zhu

  12. Department of Endocrinology, Kunshan Hospital Affiliated to Jiangsu University, Kunshan, China

    Ying Pan

  13. The Big Data Research Center, Chongqing Renji affiliated Hospital to the University of Chinese Academy of Sciences, Chongqing, China

    Shaoyun Li

  14. Ophthalmic Center, Kiang Wu Hospital, Macau, China

    Iat Fan Lai

  15. Peking University First Affiliated Hospital, Beijing, China

    Ying Chi

  16. Peking University Third Affiliated Hospital, Beijing, China

    Changuang Wang

  17. Biotherapy Center, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China

    Qi Zhang

  18. Department of Applied Biology and Chemical Technology, Hong Kong Polytechnic University, Hong Kong, China

    Johnson Lau & Dennis Lam

  19. C-MER Dennis Lam and Partners Eye Center, C-MER International Eye Care Group, Hong Kong, China

    Dennis Lam

  20. Ophthalmic Center of the First People’s Hospital of Kashi Prefecture, Kashi Prefecture, Xinjiang, China

    Xiaoguang Zou, Aizezi Wumaier & Jianquan Wang

  21. Medical Research Institute, Wuhan University, Wuhan, China

    Yin Shen

  22. Clinical Research Institue, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China

    Yong Zhou

Authors
  1. Kang Zhang
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Contributions

K. Zhang, X.L., J.X., J.Y., W.C., K.W., T.C., Y.G., S.N., X.X., X.Q., Y. Su, W.X., A.O., K.X., Z.L., M.Z., X. Zeng, C.Z., O.L., E.Z., J.Z., Y.X., D.K., K. Zhou, Y.P., S.L., I.L., Y.C., C.W., M.P., G.Z, Q.Z., J.L., D.L., X. Zou, A.W., J.W., Y. Shen, F.F.H., P.Z., T.X., Y.Z. and G.W. collected and analysed the data. K. Zhang and G.W. conceived and supervised the project. K. Zhang and G.W. wrote the manuscript with assistance from K.X. All authors discussed the results and reviewed the manuscript.

Corresponding authors

Correspondence to Kang Zhang, Ting Chen, Tao Xu, Yong Zhou or Guangyu Wang.

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Peer review information Nature Biomedical Engineering thanks Sebastian Waldstein and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Zhang, K., Liu, X., Xu, J. et al. Deep-learning models for the detection and incidence prediction of chronic kidney disease and type 2 diabetes from retinal fundus images. Nat Biomed Eng 5, 533–545 (2021). https://doi.org/10.1038/s41551-021-00745-6

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