Skip to main content

Advertisement

Springer Nature Link
Log in

DeepUWF-plus: automatic fundus identification and diagnosis system based on ultrawide-field fundus imaging

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Poor eye health is a major public health problem, and the timely detection and diagnosis of fundus abnormalities is important for eye health protection. Traditional imaging models can hinder the comprehensive evaluation of fundus abnormalities due to the use of a narrow field of view. The emerging ultrawide-field (UWF) imaging model surpasses this limitation in a non invasive, wide-view manner and is suitable for fundus observation and screening. Nevertheless, manual screening is labour intensive and subjective, especially in the absence of an ophthalmologist. Therefore, a set of auxiliary screening methods for a fundus screening service using a combination of deep learning and UWF imaging technology, which is designated as DeepUWF-Plus, is proposed. This service includes a subsystem for the screening of fundus, a subsystem for the identification of abnormalities regarding four important fundus locations, and a subsystem for the diagnosis of four retinal diseases that threaten vision. The influence of two-stage and one-stage classification strategies on the prediction performance of the model is experimentally investigated to alleviate severe class imbalance and similarity between classes, and evaluate the effectiveness and reliability of the system. Our experimental results show that DeepUWF-Plus is effective when using the two-stage strategy, especially for identifying signs or symptoms of minor diseases. DeepUWF-Plus can improve the practicality of fundus screening and enable ophthalmologists to provide more comprehensive fundus assessments.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. T. I. A. for the Prevention of Blindness (IAPB), Vision 2020. https://www.iapb.org/global-initiatives/vision-2020

  2. Ting DSW, Cheung GCM, Wong TY (2016) Diabetic retinopathy: global prevalence, major risk factors, screening practices and public health challenges: a review. Clin Exp Ophthalmol 44(4):260–277

    Article  Google Scholar 

  3. W. H. Organization, World report on vision. https://www.who.int/publicationsdetail/worldreportonvision

  4. W. H. Organization, Universal eye health: a global action plan 2014-2019. https://www.who.int/blindness/actionplan/en

  5. Li-xin, X. Some suggestions on prevention and treatment of blindness in china. Chin J Ophthalmol Med

  6. M. Y. H., Diabetic retinopathy screening rate of less than 10% in china. Chin J Med Sci 3

  7. The national plan for the prevention of blindness (2012-2015), Chinese Ministry of Health

  8. Zhao J et al (2010) Prevalence of vision impairment in older adults in rural china: the China nine-province survey. Ophthalmology 117(3):409–416

    Article  Google Scholar 

  9. Schmidt-Erfurth U, Sadeghipour A, Gerendas BS, Waldstein SM, Bogunović H (2018) Artificial intelligence in retina. Prog Retin Eye Res 67:1–29

    Article  Google Scholar 

  10. Shi L, Wu H, Dong J, Jiang K, Lu X, Shi J (2015) Telemedicine for detecting diabetic retinopathy: a systematic review and meta-analysis. Br J Ophthalmol 99(6):823–831

    Article  Google Scholar 

  11. He J, Baxter SL, Xu J, Xu J, Zhou X, Zhang K (2019) The practical implementation of artificial intelligence technologies in medicine. Nature Med 25(1):30–36

    Article  Google Scholar 

  12. Bengio Y, Courville A, Vincent P (2013) Representation learning: A review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35(8):1798–1828

    Article  Google Scholar 

  13. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436

    Article  Google Scholar 

  14. Yi Z (2010) Foundations of implementing the competitive layer model by lotka–volterra recurrent neural networks. IEEE Trans Neural Netw 21(3):494–507

    Article  Google Scholar 

  15. Chen Y, Yi Z (2019) Locality-constrained least squares regression for subspace clustering. Knowl.-Based Syst 163:51–56

    Article  Google Scholar 

  16. Chen D, Davies ME, Golbabaee M Compressive mr fingerprinting reconstruction with neural proximal gradient iterations. arXiv:2006.15271

  17. Liang H, Tsui BY, Ni H, Valentim CC, Baxter SL, Liu G, Cai W, Kermany DS, Sun X, Chen J et al (2019) Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence. Nature Med 25(3):433–438

    Article  Google Scholar 

  18. Li Z, He Y, Keel S, Meng W, Chang RT, He M (2018) Efficacy of a deep learning system for detecting glaucomatous optic neuropathy based on color fundus photographs. Ophthalmology 125 (8):1199–1206

    Article  Google Scholar 

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

    Article  Google Scholar 

  20. Pan SJ, Yang Q et al (2010) A survey on transfer learning. IEEE Transactions on knowledge and data engineering 22(10):1345–1359

    Article  Google Scholar 

  21. Michalski RS, A theory and methodology of inductive learning (1983). In: Machine learning. Springer, pp 83–134

  22. Rokach L (2010) Ensemble-based classifiers. Artif Intell Rev 33(1-2):1–39

    Article  Google Scholar 

  23. Polikar R (2006) Ensemble based systems in decision making. IEEE Circ Syst Mag 6(3):21–45

    Article  Google Scholar 

  24. Opitz D, Maclin R (1999) Popular ensemble methods: An empirical study. J Artif Intell Res 11:169–198

    Article  Google Scholar 

  25. Aiello LP, Odia I, Glassman AR, Melia M, Jampol LM, Bressler NM, Kiss S, Silva PS, Wykoff CC, Sun JK (2019) Comparison of early treatment diabetic retinopathy study standard 7-field imaging with ultrawide-field imaging for determining severity of diabetic retinopathy. JAMA Ophthalmol 137 (1):65–73

    Article  Google Scholar 

  26. Silva PS, Cavallerano JD, Sun JK, Noble J, Aiello LM, Aiello LP (2012) Nonmydriatic ultrawide field retinal imaging compared with dilated standard 7-field 35-mm photography and retinal specialist examination for evaluation of diabetic retinopathy. Am J Ophthalmol 154(3):549–559

    Article  Google Scholar 

  27. Ohsugi H, Tabuchi H, Enno H, Ishitobi N (2017) Accuracy of deep learning, a machine-learning technology, using ultra–wide-field fundus ophthalmoscopy for detecting rhegmatogenous retinal detachment. Sci Rep 7(1):1–4

    Article  Google Scholar 

  28. Nagiel A et al (2016) Ultra-widefield fundus imaging: a review of clinical applications and future trends. Retina 36(4):660–678

    Article  Google Scholar 

  29. Silva PS, Horton MB, Clary D, Lewis DG, Sun JK, Cavallerano JD, Aiello LP (2016) Identification of diabetic retinopathy and ungradable image rate with ultrawide field imaging in a national teleophthalmology program. Ophthalmology 123(6):1360–1367

    Article  Google Scholar 

  30. Abràmoff MD, Lou Y, Erginay A, Clarida W, Amelon R, Folk JC, Niemeijer M (2016) Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Investig Ophthalmol Vis Sci 57(13):5200–5206

    Article  Google Scholar 

  31. Ting DSW, Cheung CY-L, Lim G, Tan GSW, Quang ND, Gan A, Hamzah H, Garcia-Franco R, San Yeo IY, Lee SY 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 

  32. Zhang W, Zhong J, Yang S, Gao Z, Hu J, Chen Y, Yi Z (2019) Automated identification and grading system of diabetic retinopathy using deep neural networks. Knowl.-Based Syst 175:12–25

    Article  Google Scholar 

  33. Burlina P, Pacheco KD, Joshi N, Freund DE, Bressler NM (2017) Comparing humans and deep learning performance for grading amd: a study in using universal deep features and transfer learning for automated amd analysis. Comput Biol Med 82:80–86

    Article  Google Scholar 

  34. Raghavendra U, Fujita H, Bhandary SV, Gudigar A, Tan JH, Acharya UR (2018) Deep convolution neural network for accurate diagnosis of glaucoma using digital fundus images. Inform Sci 441:41–49

    Article  MathSciNet  Google Scholar 

  35. Asaoka R, Murata H, Iwase A, Araie M (2016) Detecting preperimetric glaucoma with standard automated perimetry using a deep learning classifier. Ophthalmology 123(9):1974–1980

    Article  Google Scholar 

  36. Topol EJ (2019) High-performance medicine: the convergence of human and artificial intelligence. Nature Med 25(1):44

    Article  Google Scholar 

  37. Li Z, Guo C, Nie D, Lin D, Zhu Y, Chen C, Wu X, Xu F, Jin C, Zhang X et al (2020) Deep learning for detecting retinal detachment and discerning macular status using ultra-widefield fundus images. Commun Biol 3(1):1–10

    Article  Google Scholar 

  38. Matsuba S, Tabuchi H, Ohsugi H, Enno H, Ishitobi N, Masumoto H, Kiuchi Y (2018) Accuracy of ultra-wide-field fundus ophthalmoscopy-assisted deep learning, a machine-learning technology, for detecting age-related macular degeneration. Int Ophthalmol 1–7

  39. Nagasato D, Tabuchi H, Ohsugi H, Masumoto H, Enno H, Ishitobi N, Sonobe T, Kameoka M, Niki M, Mitamura Y (2019) Deep-learning classifier with ultrawide-field fundus ophthalmoscopy for detecting branch retinal vein occlusion. Int J Opthalmol 12(1):98–103

    Google Scholar 

  40. Nagasato D, Tabuchi H, Ohsugi H, Masumoto H, Enno H, Ishitobi N, Sonobe T, Kameoka M, Niki M, Hayashi K et al (2018) Deep neural network-based method for detecting central retinal vein occlusion using ultrawide-field fundus ophthalmoscopy. J Ophthalmol

  41. Choi JY, Yoo TK, Seo JG, Kwak J, Um TT, Rim TH (2017) Multi-categorical deep learning neural network to classify retinal images: A pilot study employing small database. PloS One 12(11):e0187336

    Article  Google Scholar 

  42. Fenner BJ, Wong RL, Lam WC, Tan GS, Cheung GC (2018) Advances in retinal imaging and applications in diabetic retinopathy screening: a review. Ophthalmol Ther 7(2):333–346

    Article  Google Scholar 

  43. Lynch SK, Shah A, Folk JC, Wu X, Abramoff MD (2017) Catastrophic failure in image-based convolutional neural network algorithms for detecting diabetic retinopathy. Investig Ophthalmol Vis Sci 58(8):3776–3776

    Google Scholar 

  44. Bagheri N, Wajda B, Calvo C, Durrani A (2016) The Wills eye manual: office and emergency room diagnosis and treatment of eye disease. Lippincott Williams & Wilkins, Philadelphia

    Google Scholar 

  45. Chang JS, Smiddy WE (2014) Cost-effectiveness of retinal detachment repair. Ophthalmology 121(4):946–951

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported by the National Key Research and Development Program of China under Grant 2018AAA0100201, the National Natural Science Foundation of China under Grant 61702349, and the Sichuan Province Science and Technology Support Program 2019YFS0246.

Author information

Authors and Affiliations

  1. Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, 610065, People’s Republic of China

    Wei Zhang, Yuanyuan Chen & Zhang Yi

  2. Mianyang Central Hospital, Mianyang, Sichuan Province, 621000, China

    Yan Dai

  3. School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610072, China

    Miao Liu & Jie Zhong

  4. The Department of Ophthalmology, Sichuan Academy of Medical Sciences and Sichuan Provincial Peoples Hospital, Chengdu, 610072, China

    Miao Liu & Jie Zhong

Authors
  1. Wei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yan Dai
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Miao Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yuanyuan Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jie Zhong
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Zhang Yi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Yuanyuan Chen or Jie Zhong.

Ethics declarations

Conflict of Interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher’s note

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

Wei Zhang and Yan Dai are co-first authors

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, W., Dai, Y., Liu, M. et al. DeepUWF-plus: automatic fundus identification and diagnosis system based on ultrawide-field fundus imaging. Appl Intell 51, 7533–7551 (2021). https://doi.org/10.1007/s10489-021-02242-4

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-021-02242-4

Keywords