Skip to main content

Advertisement

SpringerLink
Log in

Deep learning-based automated detection of glaucomatous optic neuropathy on color fundus photographs

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

Abstract

Purpose

To develop a deep learning approach based on deep residual neural network (ResNet101) for the automated detection of glaucomatous optic neuropathy (GON) using color fundus images, understand the process by which the model makes predictions, and explore the effect of the integration of fundus images and the medical history data from patients.

Methods

A total of 34,279 fundus images and the corresponding medical history data were retrospectively collected from cohorts of 2371 adult patients, and these images were labeled by 8 glaucoma experts, in which 26,585 fundus images (12,618 images with GON-confirmed eyes, 1114 images with GON-suspected eyes, and 12,853 NORMAL eye images) were included. We adopted 10-fold cross-validation strategy to train and optimize our model. This model was tested in an independent testing dataset consisting of 3481 images (1524 images from NORMAL eyes, 1442 images from GON-confirmed eyes, and 515 images from GON-suspected eyes) from 249 patients. Moreover, the performance of the best model was compared with results obtained by two experts. Accuracy, sensitivity, specificity, kappa value, and area under receiver operating characteristic (AUC) were calculated. Further, we performed qualitative evaluation of model predictions and occlusion testing. Finally, we assessed the effect of integrating medical history data in the final classification.

Results

In a multiclass comparison between GON-confirmed eyes, GON-suspected eyes and NORMAL eyes, our model achieved 0.941 (95% confidence interval [CI], 0.936–0.946) accuracy, 0.957 (95% CI, 0.953–0.961) sensitivity, and 0.929 (95% CI, 0.923–0.935) specificity. The AUC distinguishing referrals (GON-confirmed and GON-suspected eyes) from observation was 0.992 (95% CI, 0.991–0.993). Our best model had a kappa value of 0.927, while the two experts’ kappa values were 0.928 and 0.925 independently. The best 2 binary classifiers distinguishing GON-confirmed/GON-suspected eyes from NORMAL eyes obtained 0.955, 0.965 accuracy, 0.977, 0.998 sensitivity, and 0.929, 0.954 specificity, while the AUC was 0.992, 0.999 respectively. Additionally, the occlusion testing showed that our model identified the neuroretinal rim region, retinal nerve fiber layer (RNFL) defect areas (superior or inferior) as the most important parts for the discrimination of GON, which evaluated fundus images in a way similar to clinicians. Finally, the results of integration of fundus images with medical history data showed a slight improvement in sensitivity and specificity with similar AUCs.

Conclusions

This approach could discriminate GON with high accuracy, sensitivity, specificity, and AUC using color fundus photographs. It may provide a second opinion on the diagnosis of glaucoma to the specialist quickly, efficiently and at low cost, and assist doctors and the public in large-scale screening for glaucoma.

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
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Al-Bander B, Williams BM, Al-Nuaimy W et al (2018) Dense fully convolutional segmentation of the optic disc and cup in colour fundus for glaucoma diagnosis. Symmetry-Basel 10(4):87. https://doi.org/10.3390/sym10040087

  2. Bock R, Meier J, Nyul LG et al (2010) Glaucoma risk index: automated glaucoma detection from color fundus images. Med Image Anal 14(3):471–481. https://doi.org/10.1016/j.media.2009.12.006

    Article  PubMed  Google Scholar 

  3. Tham YC, Li X, Wong TY et al (2014) Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-ayalysis. Ophthalmology 121(11):2081–2090. https://doi.org/10.1016/j.ophtha.2014.05.013

    Article  PubMed  Google Scholar 

  4. Quigley HA, Broman AT (2006) The number of people with glaucoma worldwide in 2010 and 2020. Br J Ophthalmol 90(3):262–267. https://doi.org/10.1136/bjo.2005.081224

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Orlando JI, Prokofyeva E, Fresno M et al (2017) Convolutional neural network transfer for automated glaucoma identification. Proceeding of SPIE 10160:UNSP 101600U. https://doi.org/10.1117/12.2255740

  6. Rashmi P, Niladri B, Aparna R et al (2018) Deep convolutional neural network-based path classification for retinal nerve fiber layer defect detection in early glaucoma. J Med Imag 5(4):044003. https://doi.org/10.1117/1.JMI.5.4.044003

    Article  Google Scholar 

  7. Lee CS, Baughman DM, Lee AY (2017) Deep learning is effective for classifying normal versus age-related macular degeneration optical coherence tomography images. Ophthalmol Retina 1(4):322–327. https://doi.org/10.1016/j.oret.2016.12.009

    Article  PubMed  PubMed Central  Google Scholar 

  8. Li Z, He YF, Keel S et al (2018) Efficacy of a deep learning system for detecting glaucomatous optic neuropathy based on color fundus photographs. Ophthalmology 125(8):1199–1206. https://doi.org/10.1016/j.ophtha.2018.01.023

    Article  PubMed  Google Scholar 

  9. Phan S, Satoh S, Yoda Y et al (2019) Evaluation of deep convolutional neural networks for glaucoma detection. Jpn J Ophthalmol 63(3):276–283. https://doi.org/10.1007/s10384-019-00659-6

    Article  PubMed  Google Scholar 

  10. Shibata N, Tanito M, Mitsuashi K et al (2018) Development of a deep residual learning algorithm to screen for glaucoma from fundus photography. Sci Rep 8:14665 https://www.nature.com/articles/s41598-018-33013-w.pdf

    Article  PubMed  PubMed Central  Google Scholar 

  11. Diaz-Pinto MS, Naranjo V et al (2019) CNNs for automatic glaucoma assessment using fundus images: an extensive validation. Biomed Eng Online 18:29. https://doi.org/10.1186/s12938-019-0649-y

    Article  PubMed  PubMed Central  Google Scholar 

  12. Turajlic E, Karahodzic V (2017) An adaptive scheme for X-ray medical image denoising using artificial neural networks and additive white gaussian noise level estimation in SVD domain. CMBEBIH 62:36–40. https://doi.org/10.1007/978-981-10-4166-2_7

    Article  Google Scholar 

  13. Armaly MF, Sayegh RE (1969) The cup/disc ratio: the findings of tonometry and tonography in the normal eye. Arch Ophthalmol 82:191–196. https://doi.org/10.1001/archopht.1969.00990020193008

    Article  CAS  PubMed  Google Scholar 

  14. Jonas JB, Gusek GC, Naumann GO (1988) Optic disc, cup and neuro-retinal rim size, configuration, and correlations in normal eyes. Invest Ophthalmol Vis Sci 29:1151–1158 http://iovs.arvojournals.org/data/journals/iovs/933371/1151.pdf

    CAS  PubMed  Google Scholar 

  15. Deng Z, Li Y, Wang Y et al (2017) Tracking within hadronic showers in the CALICE SDHCAL prototype using a Hough transform technique. J Instrum 12:P05009 https://biblio.ugent.be/publication/8568622/file/8568804.pdf

    Article  Google Scholar 

  16. Han DM, Liu QG, Fan WG (2018) A new image classification method using CNN transfer learning and web data augmentation. Expert Syst Appl 95:43–56. https://doi.org/10.1016/j.eswa.2017.11.028

    Article  Google Scholar 

  17. Mahapatra D, Roy PK, Sedai S et al (2016) Retinal image quality classification using saliency maps and CNNs. Machine Learning in Medical Imaging 10019:172–179. https://doi.org/10.1007/978-3-319-47157-0_21

  18. Yao LY, Li T, Ye YS et al (2016) Image super-resolution based on self-similarity and various patch size. ICDIP 10033(1):UNSP 100334F. https://doi.org/10.1117/12.2243771

  19. He KM, Zhang XY, Ren SQ et al (2016) Deep residual learning for image recognition. P- ublish In CVPR 16541111:770–778. https://doi.org/10.1109/CVPR.2016.90

  20. Paoletti ME, Haut JM, Plaza J et al (2018) Deep & dense convolutional neural network for hyperspectral image classification. Remote Sens 10(9):1454. https://doi.org/10.3390/rs10091454

    Article  Google Scholar 

  21. Medeiros FA, Alessandro A et al (2019) From machine to machine an OCT-trained deep learning algorithm for objective quantification of glaucomatous damage in fundus photographs. Ophthalmol 126(4):513–521. https://doi.org/10.1016/j.ophtha.2018.12.033

    Article  Google Scholar 

  22. Keel S, Wu JR, Lee PY et al (2019) Visualizing deep learning models for the detection of referable diabetic retinopathy and glaucoma. JAMA Ophthalmol 137(3):288–292. https://doi.org/10.1001/jamaophthalmol.2018.6035

    Article  PubMed  Google Scholar 

  23. Christopher M, Belghith A, Bowd C et al (2018) Performance of deep learning architectures and transfer learning for detecting glaucomatous optic neuropathy in fundus photographs. Sci Rep 8:16685 https://www.nature.com/articles/s41598-018-35044-9.pdf

    Article  PubMed  PubMed Central  Google Scholar 

  24. Gomez-Valverde JJ, Anton A, Fatti G et al (2019) Automatic glaucoma classification using color fundus images based on convolutional neural networks and transfer learning. Biomed Opt Express 10(2):892–913. https://doi.org/10.1364/BOE.10.000892

    Article  PubMed  PubMed Central  Google Scholar 

  25. Lima ACD, Maia LB, Pereira RMP et al (2018) Glaucoma diagnosis over eye fundus image through deep features. 25th international conference on systems, signals and image processing (IWSSIP). Accessed 22 JUN 2018. https://doi.org/10.1109/IWSSIP.2018.8439477

  26. Coleman AL, Miglior S (2008) Risk factors for glaucoma onset and progression. Surv Ophthalmol 53(6 Suppl 1):S3–S10. https://doi.org/10.1016/j.survophthal.2008.08.006

    Article  PubMed  Google Scholar 

  27. Weinreb RN, Aung T, Medeiros FA (2014) The pathophysiology and treat of glaucoma: a review. Jama 311(18):1901–1911. https://doi.org/10.1001/jama.2014.3192

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Shen DG, Wu GR, Suk H et al (2017) Deep learning in medical image analysis. Annu Rev Biomed Eng 19:221–248 http://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC5479722&blobtype=pdf

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Miller SE, Thapa S, Robin AL et al (2017) Glaucoma screening in Nepal: cup-to-disc estimate with standard Mydriatic fundus camera compared to portable nonmydriatic camera. Am J Ophthalmol 182:99–106. https://doi.org/10.1016/j.ajo.2017.07.010

    Article  PubMed  PubMed Central  Google Scholar 

  30. Krishnan MMR, Faust O (2013) Automated glaucoma detection using hybrid feature extraction in retinal fundus images. J Mech Med Biol 13(01):135001. https://doi.org/10.1142/S0219519413500115

    Article  Google Scholar 

  31. Maheshwari S, Pachori RB, Acharya UR (2017) Automated diagnosis of glaucoma using empirical wavelet transform and correntropy features extracted from fundus images. IEEE J Biomed Health Inform 21(3):803–813. https://doi.org/10.1109/JBHI.2016.2544961

    Article  PubMed  Google Scholar 

  32. Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. International Conference on Learning Representations Accessed 9 ay 2015. https://arxiv.org/pdf/1409.1556.pdf

  33. Son J, Shin JY, Kim HD et al (2019) Development and validation of deep learning models for screening multiple abnormal findings in retinal fundus images. Ophthalmology Accessed 24 May 2019. https://doi.org/10.1016/j.ophtha.2019.05.029

  34. Chalakkal RJ, Abdulla WH, Thulaseedharan SS et al (2019) Quality and content analysis of fundus images using deep learning. Comput Biol Med 108:317–331. https://doi.org/10.1016/j.compbiomed.2019.03.019

    Article  PubMed  Google Scholar 

  35. Ye DP, Jiang SZ, Li SY et al (2019) Faster and transferable deep learning steganalysis on GPU. J Real-Time Image Proc 16(3):623–633. https://doi.org/10.1007/s11554-019-00870-1

    Article  Google Scholar 

  36. Tsai IL, Tsai CY, Kuo LL et al (2012) Transient changes of intraocular pressure and anterior segment configuration after diagnostic mydriasis with 1% tropicamide in children. Clin Exp Optom 95(2):166–172. https://doi.org/10.1111/j.1444-0938.2011.00677.x

    Article  PubMed  Google Scholar 

Download references

Funding

This study was funded by the National Key Research and Development Program of China (2016YFF0101400), the National Natural Science Foundation of China (51675321), and the National Natural Science Foundation of China (61905144).

Author information

Authors and Affiliations

  1. School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China

    Feng Li, Lei Yan, Yuguang Wang, Jianxun Shi, Hua Chen, Xuedian Zhang & Minshan Jiang

  2. Department of Precision Mechanical Engineering, Shanghai University, Shanghai, 200072, China

    Zhizheng Wu

  3. Liver Cancer Institute, Zhongshan Hospital, Shanghai, 200032, China

    Kaiqian Zhou

Authors
  1. Feng Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lei Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yuguang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jianxun Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hua Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xuedian Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Minshan Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Zhizheng Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Kaiqian Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Minshan Jiang.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

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

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

Li, F., Yan, L., Wang, Y. et al. Deep learning-based automated detection of glaucomatous optic neuropathy on color fundus photographs. Graefes Arch Clin Exp Ophthalmol 258, 851–867 (2020). https://doi.org/10.1007/s00417-020-04609-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00417-020-04609-8

Keywords

Search

Navigation