Skip to main content

Advertisement

SpringerLink
Log in

Automated detection of severe diabetic retinopathy using deep learning method

  • Retinal Disorders
  • Published:
Graefe's Archive for Clinical and Experimental Ophthalmology Aims and scope Submit manuscript

Abstract

Purpose

The purpose of this study is to develop and validate the intelligent diagnosis of severe DR with lesion recognition based on color fundus photography.

Methods

The Kaggle public dataset for DR grading is used in the project, including 53,576 fundus photos in the test set, 28,101 in the training set, and 7,025 in the validation set. We randomly select 4,192 images for lesion annotation. Inception V3 structure is adopted as the classification algorithm. Both 299 × 299 pixel images and 896 × 896 pixel images are used as the input size. ROC curve, AUC, sensitivity, specificity, and their harmonic mean are used to evaluate the performance of the models.

Results

The harmonic mean and AUC of the model of 896 × 896 input are higher than those of the 299 × 299 input model. The sensitivity, specificity, harmonic mean, and AUC of the method with 896 × 896 resolution images as input for severe DR are 0.925, 0.907, 0.916, and 0.968, respectively. The prediction error mainly occurs in moderate NPDR, and cases with more hard exudates and cotton wool spots are easily predicted as severe cases. Cases with preretinal hemorrhage and vitreous hemorrhage are easily identified as severe cases, and IRMA is the most difficult lesion to recognize.

Conclusions

We have studied the intelligent diagnosis of severe DR based on color fundus photography. This artificial intelligence–based technology offers a possibility to increase the accessibility and efficiency of severe DR screening.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Yang W, Lu J, Weng J et al (2010) Prevalence of diabetes among men and women in China. N Engl J Med 362(12):1090–1101

    Article  CAS  Google Scholar 

  2. Chan JC, Zhang Y, Ning G (2014) Diabetes in China: a societal solution for a personal challenge. Lancet Diabetes Endocrinol 2(1):969–979

    Article  Google Scholar 

  3. Bellemo V, Lim G, Rim TH et al (2019) Artificial intelligence screening for diabetic retinopathy: the real-world emerging application. Curr Diab Rep 19(9):72

    Article  Google Scholar 

  4. Knuth DE (1997) The Art of Computer Programming, Volume 2: Seminumerical Algorithms. Addison Wesley Longman, 145–146

  5. He K, Zhang X, Ren S, et al (2016) Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition. 770–778.

  6. Li Z, Keel S, Liu C et al (2018) An automated grading system for detection of vision-threatening referable diabetic retinopathy on the basis of color fundus photographs. Diabetes Care 41(12):2509–2516

    Article  Google Scholar 

  7. Abràmoff MD, Lou Y, Erginay A et al (2016) Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Invest Ophthalmol Vis Sci 57(13):5200–5206

    Article  Google Scholar 

  8. Gulshan V, Peng L, Coram M et al (2016) Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316(22):2402–2410

    Article  Google Scholar 

  9. Gargeya R, Leng T (2017) Automated identification of diabetic retinopathy using deep learning. Ophthalmology 124(7):962–969

    Article  Google Scholar 

  10. Ting DSW, Cheung CY, Lim G et al (2017) Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. JAMA 318(22):2211–2223

    Article  Google Scholar 

  11. Abràmoff MD, Lavin PT, Birch M et al (2018) Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. NPJ Digit Med 1:39

    Article  Google Scholar 

  12. Ting DSW, Pasquale LR, Peng L et al (2019) Artificial intelligence and deep learning in ophthalmology. Br J Ophthalmol 103(2):167–175

    Article  Google Scholar 

  13. Arcadu F, Benmansour F, Maunz A et al (2019) Deep learning predicts OCT measures of diabetic macular thickening from color fundus photographs. Invest Ophthalmol Vis Sci 60(4):852–857

    Article  Google Scholar 

Download references

Funding

1. The Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences (2018PT32029)

2. CAMS Initiative for Innovative Medicine(CAMS-I2M, 2018-I2M-AI-001)

3. Pharmaceutical collaborative innovation research project of Beijing Science and Technology Commission (Z191100007719002)

4. National Key Research and Development Project(SQ2018YFC200148)

5. Beijing Natural Science Foundation Haidian original innovation joint fund (19L2062)

6. National Natural Science Foundation of China (NSFC No. 61672523)

7. Beijing Natural Science Foundation (BJNSF No. 4202033)

Author information

Author notes

  1. Xiao Zhang and Fan Li contributed equally to this article.

Authors and Affiliations

  1. Department of Ophthalmology, Union Medical College Hospital, Chinese Academy of Medical Sciences, PekingBeijing, China

    Xiao Zhang, Xiaoxu Han, Bilei Zhang, Huan Chen, Weihong Yu & Youxin Chen

  2. Key Laboratory of Ocular Fundus Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China

    Xiao Zhang, Xiaoxu Han, Bilei Zhang, Huan Chen, Weihong Yu & Youxin Chen

  3. Key Lab of Data Engineering and Knowledge Engineering, Renmin University of China, Beijing, China

    Fan li & Xirong Li

  4. Vistel AI Lab, Visionary Intelligence Ltd, Beijing, China

    Donghong Li, Qijie Wei & Dayong Ding

  5. Ophthalmology and Visual Science Key Lab, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, BeijingBeijing, China

    Yongpeng Zhang & Bin Mo

  6. Department of Retina, Tianjin Medical University Eye Hospital, Tianjin, China

    Bojie Hu

Authors
  1. Xiao Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fan li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Donghong Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Qijie Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xiaoxu Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Bilei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Huan Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Yongpeng Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Bin Mo
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Bojie Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Dayong Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Xirong Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Weihong Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Youxin Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Weihong Yu or Youxin Chen.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Ethical approval

Procedures performed in this study were in accordance with the ethical standards of the Peking Union Medical College Hospital and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, X., li, F., Li, D. et al. Automated detection of severe diabetic retinopathy using deep learning method. Graefes Arch Clin Exp Ophthalmol 260, 849–856 (2022). https://doi.org/10.1007/s00417-021-05402-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00417-021-05402-x

Keywords

Search

Navigation