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DDLA: a double deep latent autoencoder for diabetic retinopathy diagnose based on continuous glucose sensors

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Abstract

The current diagnosis of diabetic retinopathy is based on fundus images and clinical experience. However, considering the ineffectiveness and non-portability of medical devices, we aimed to develop a diagnostic model for diabetic retinopathy based on glucose series data from the wearable continuous glucose monitoring system. Therefore, this study developed a novel method, i.e., double deep latent autoencoder, for exploring glycemic variability influence from multi-day glucose data for diabetic retinopathy. Specifically, the model proposed in this research could encode continuous glucose sensor data with non-continuous and variable length via the integration of a data reorganization module and a novel encoding module with fragmented-missing-wise objective function. Additionally, the model implements a double deep autoencoder, which integrated convolutional neural network, long short-term memory, to jointly capturing the inter-day and intra-day glucose latent features from glucose series. The effectiveness of the proposed model is evaluated through a cross-validation method to clinical datasets of 765 type 2 diabetes patients. The proposed method achieves the highest accuracy value (0.89), precision value (0.88), and F1 score (0.73). The results suggest that our model can be used to remotely diagnose and screen for diabetic retinopathy by learning potential features of glucose series data collected by wearable continuous glucose monitoring systems.

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Acknowledgements

We would like to thank all the involved clinicians, nurses, and technicians at Shanghai Clinical Center for Diabetes for dedicating their time and skills to the completion of this study. The authors would like to thank to the editors and anonymous reviewers for their constructive comments and suggestions that have improved the quality of the study.

Funding

This work was supported by the National Natural Science Foundation of China Youth Fund Project (61903071); the National Natural Science Foundation of China (61973067), the Program of Shanghai Academic/Technology Research Leader (22XD1402300), and the Shanghai “Rising Stars of Medical Talent” Youth Development Program–Outstanding Youth Medical Talents (SHWSRS(2021)_099).

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Authors and Affiliations

Authors

Contributions

R.T designed the algorithm, analyzed the data, and wrote the paper. R.T, J.Z, J.L, X.Y, and H.L conceived the study and revised the manuscript. J.L, W.L, and Y.W conducted the study and collected the data. Y.H and X.S analyzed the data. X.Y, J.Z, and H.L are the guarantors of this work. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Jian Zhou or Xia Yu.

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Informed consent was obtained from individual participants included in the study.

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Patients signed informed consent regarding publishing their data.

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Highlights

• The proposed model is the first end-to-end CGM based DR diagnostic model.

• The novel design of autoencoder proposed the model to extract intra/inter-day features from CGM time series data with fragmentation missing.

• The performance of the model has been validated on real-clinical dataset.

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Supplementary file1 (DOCX 651 kb)

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Tao, R., Li, H., Lu, J. et al. DDLA: a double deep latent autoencoder for diabetic retinopathy diagnose based on continuous glucose sensors. Med Biol Eng Comput 62, 3089–3106 (2024). https://doi.org/10.1007/s11517-024-03120-0

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