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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


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: KaplanMeier 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.

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 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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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.

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Authors and Affiliations



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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The authors declare no competing interests.

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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).

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