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Identification of diabetic retinopathy classification using machine learning algorithms on clinical data and optical coherence tomography angiography

Eye volume 38, pages 2813–2821 (2024)Cite this article

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Abstract

Background

To apply machine learning (ML) algorithms to perform multiclass diabetic retinopathy (DR) classification using both clinical data and optical coherence tomography angiography (OCTA).

Methods

In this cross-sectional observational study, clinical data and OCTA parameters from 203 diabetic patients (203 eye) were used to establish the ML models, and those from 169 diabetic patients (169 eye) were used for independent external validation. The random forest, gradient boosting machine (GBM), deep learning and logistic regression algorithms were used to identify the presence of DR, referable DR (RDR) and vision-threatening DR (VTDR). Four different variable patterns based on clinical data and OCTA variables were examined. The algorithms’ performance were evaluated using receiver operating characteristic curves and the area under the curve (AUC) was used to assess predictive accuracy.

Results

The random forest algorithm on OCTA+clinical data-based variables and OCTA+non-laboratory factor-based variables provided the higher AUC values for DR, RDR and VTDR. The GBM algorithm produced similar results, albeit with slightly lower AUC values. Leading predictors of DR status included vessel density, retinal thickness and GCC thickness, as well as the body mass index, waist-to-hip ratio and glucose-lowering treatment.

Conclusions

ML-based multiclass DR classification using OCTA and clinical data can provide reliable assistance for screening, referral, and management DR populations.

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Fig. 1: ROC curves with sensitivity and specificity for the diabetic retinopathy classification in different patterns.
Fig. 2: Top 10 predictors contributed to the diabetic retinopathy classification based on four patterns in the random forest model.

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Data availability

The data used in the current study is available from the corresponding author upon request.

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Funding

This work was supported by the National Natural Science Foundation of China (grant numbers 82171072), the Guangdong Basic and Applied Basic Research Foundation (grant numbers 2021A1515010921, 2022A1515012073), the Guangzhou Science and Technology Program Project (grant numbers 202002030400); and the Sun Yat-sen Clinical Research Cultivating Program (grant numbers SYS-Q-202104).

Author information

Author notes

  1. These authors contributed equally: Xiaoli Li, Xin Wen.

  2. These authors jointly supervised this work: Yuqing Lan, Qianli Meng.

Authors and Affiliations

  1. Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China

    Xiaoli Li, Xianwen Shang, Junbin Liu, Liang Zhang, Ying Cui, Xiaoyang Luo, Zheng Lyu, Xiyu Wu & Qianli Meng

  2. Department of Ophthalmology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China

    Xin Wen & Yuqing Lan

  3. Statistics Section, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong, China

    Guanrong Zhang

  4. Department of Ophthalmology, Heyuan People’s Hospital, Heyuan, China

    Jie Xie

  5. Department of Ophthalmology, Zhujiang Hospital, Southern Medical University, Guangzhou, China

    Tian Huang

  6. Department of Ophthalmology, The Fourth Affiliated Hospital of Guangzhou Medical University, Guangzhou, China

    Zhifan Chen

Authors
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Contributions

XL and XW collected and analysed the data, interpreted the results and wrote the manuscript. XS, QM, XL and GZ analysed and interpreted the data. LZ, YC, XL, YL and QM contributed to discussion and reviewed the manuscript. JL, JX, TH, ZC, ZL and XW collected the data. QM and YL designed this study, analysed the data, interpreted the results and revised the manuscript. All authors approved the final version of the manuscript. QM and YL are the guarantors of this work and had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Corresponding authors

Correspondence to Yuqing Lan or Qianli Meng.

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Li, X., Wen, X., Shang, X. et al. Identification of diabetic retinopathy classification using machine learning algorithms on clinical data and optical coherence tomography angiography. Eye 38, 2813–2821 (2024). https://doi.org/10.1038/s41433-024-03173-3

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