Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Deep learning in glaucoma with optical coherence tomography: a review

A Correction to this article was published on 23 October 2020

This article has been updated

Abstract

Deep learning (DL), a subset of artificial intelligence (AI) based on deep neural networks, has made significant breakthroughs in medical imaging, particularly for image classification and pattern recognition. In ophthalmology, applying DL for glaucoma assessment with optical coherence tomography (OCT), including OCT traditional reports, two-dimensional (2D) B-scans, and three-dimensional (3D) volumetric scans, has increasingly raised research interests. Studies have demonstrated that using DL for interpreting OCT is efficient, accurate, and with good performance for discriminating glaucomatous eyes from normal eyes, suggesting that incorporation of DL technology in OCT for glaucoma assessment could potentially address some gaps in the current practice and clinical workflow. However, further research is crucial in tackling some existing challenges, such as annotation standardization (i.e., setting a standard for ground truth labelling among different studies), development of DL-powered IT infrastructure for real-world implementation, prospective validation in unseen datasets for further evaluation of generalizability, cost-effectiveness analysis after integration of DL, the AI “black box” explanation problem. This review summarizes recent studies on the application of DL on OCT for glaucoma assessment, identifies the potential clinical impact arising from the development and deployment of the DL models, and discusses future research directions.

摘要

深度学习 (DL) 作为基于深层神经网络的人工智能 (AI) 的一个子集, 在医学成像领域, 特别是图像分类和模式识别方面, 已取得重大突破。在眼科领域, 将DL应用于光学相干断层扫描 (OCT), 包括传统的OCT报告、二维B扫描和三维立体扫描, 从而对青光眼进行评估已引发了越来越多的研究兴趣。研究表明, 应用DL对 OCT的结果进行解读是有效、准确的, 并且能很好地区分青光眼和正常眼, 这表明DL技术与OCT结合对青光眼进行评估可弥补当前实践和临床流程中的一些空白。然而, 对于一些现存的挑战, 进一步研究是至关重要的, 例如注释标准化 (即在不同的研究中设定基础事实标签的标准), 为实际应用开发基于DL支持的IT基础架构, 在不可见的数据集中进行前瞻性验证以进一步评估泛化能力, 整合DL后的成本效益分析, 以及AI“黑箱”问题解释。本综述总结了应用DL在OCT评估青光眼的最新研究进展, 确定DL模型的开发和部署所带来的潜在临床影响, 并对未来的研究方向进行了讨论。

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Posterior segment optical coherence tomography (OCT) not only enables the top view of the retina and optic nerve head, but also captures deeper and three-dimensional (3D) view of the morphological features.
Fig. 2: Illustration of basic process of a deep learning system development and validation.
Fig. 3: There were four categories of deep learning (DL) models with different input.
Fig. 4: A potential clinical workflow with deployment of deep learning-based clinical support system for glaucoma detection with OCT images in primary, secondary and tertiary settings.
Fig. 5: Examples of heatmap generated by class activation map (CAM) for glaucomatous optic neuropathy (GON) detection generated with a previously published DL algorithm [53].

Change history

  • 23 October 2020

    An amendment to this paper has been published and can be accessed via a link at the top of the paper.

References

  1. 1.

    Osborne NN, Wood JPM, Chidlow G, Bae JH, Melena J, Nash MS. Ganglion cell death in glaucoma: what do we really know? Brit J Ophthalmol. 1999;83:980–6.

    CAS  Article  Google Scholar 

  2. 2.

    Quigley HA. Neuronal death in glaucoma. Prog Retin Eye Res. 1999;18:39–57.

    CAS  PubMed  Article  Google Scholar 

  3. 3.

    Nicoara S. The mechanisms of neuronal death in glaucoma. Oftalmologia. 2000;51:4–6.

    CAS  PubMed  Google Scholar 

  4. 4.

    Tham YC, Li X, Wong TY, Quigley HA, Aung T, Cheng CY. Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis. Ophthalmology. 2014;121:2081–90.

    PubMed  Article  Google Scholar 

  5. 5.

    Weinreb RN, Leung CKS, Crowston JG, Medeiros FA, Friedman DS, Wiggs JL, et al. Primary open-angle glaucoma. Nat Rev Dis Primers. 2016;2:16067.

    PubMed  Article  Google Scholar 

  6. 6.

    Jonas JB, Aung T, Bourne RR, Bron AM, Ritch R, Panda-Jonas S. Glaucoma. Lancet. 2017;390(10108):2183–93.

    PubMed  Article  Google Scholar 

  7. 7.

    Quigley HA, West SK, Rodriguez J, Munoz B, Klein R, Snyder R. The prevalence of glaucoma in a population-based study of Hispanic subjects: Proyecto VER. Arch Ophthalmol. 2001;119:1819–26.

    CAS  PubMed  Article  Google Scholar 

  8. 8.

    Rotchford AP, Kirwan JF, Muller MA, Johnson GJ, Roux P. Temba glaucoma study: a population-based cross-sectional survey in urban South Africa. Ophthalmology. 2003;110:376–82.

    PubMed  Article  Google Scholar 

  9. 9.

    Topouzis F, Coleman AL, Harris A, Koskosas A, Founti P, Gong G, et al. Factors associated with undiagnosed open-angle glaucoma: the Thessaloniki Eye Study. Am J Ophthalmol. 2008;145:327–35.

    PubMed  Article  Google Scholar 

  10. 10.

    Shaikh Y, Yu F, Coleman AL. Burden of undetected and untreated glaucoma in the United States. Am J Ophthalmol. 2014;158:1121–9 e1.

    PubMed  Article  Google Scholar 

  11. 11.

    Chua J, Baskaran M, Ong PG, Zheng Y, Wong TY, Aung T, et al. Prevalence, risk factors, and visual features of undiagnosed glaucoma: the singapore epidemiology of eye diseases study. JAMA Ophthalmol. 2015;133:938–46.

    PubMed  Article  Google Scholar 

  12. 12.

    Salowe R, Salinas J, Farbman NH, Mohammed A, Warren JZ, Rhodes A, et al. Primary open-angle glaucoma in individuals of African descent: a review of risk factors. J Clin Exp Ophthalmol. 2015;6:450.

    PubMed  PubMed Central  Google Scholar 

  13. 13.

    Schuman JS, Hee MR, Arya AV, Pedut-Kloizman T, Puliafito CA, Fujimoto JG, et al. Optical coherence tomography: a new tool for glaucoma diagnosis. Curr Opin Ophthalmol. 1995;6:89–95.

    CAS  PubMed  Article  Google Scholar 

  14. 14.

    Schweitzer C, Le Goff M, Korobelnik JF, Rougier MB, Delyfer MN, Dartigues JF, et al. Screening of glaucoma using spectral-domain optical coherence tomography (Sd-Oct) in an elderly population: the alienor study. Invest Ophth Vis Sci. 2015;56:1025.

    Google Scholar 

  15. 15.

    Klein BE, Johnson CA, Meuer SM, Lee K, Wahle A, Lee KE, et al. Nerve fiber layer thickness and characteristics associated with glaucoma in community living older adults: prelude to a screening trial? Ophthalmic Epidemiol. 2017;24:104–10.

    PubMed  Article  Google Scholar 

  16. 16.

    Blumberg DM, Vaswani R, Nong E, Al-Aswad L, Cioffi GA. A comparative effectiveness analysis of visual field outcomes after projected glaucoma screening using SD-OCT in African American communities. Invest Ophthalmol Vis Sci. 2014;55:3491–500.

    PubMed  PubMed Central  Article  Google Scholar 

  17. 17.

    Leung CKS, Cheung CYL, Weinreb RN, Liu S, Ye C, Lai G, et al. Evaluation of retinal nerve fiber layer progression in Glaucoma A comparison between the fast and the regular retinal nerve fiber layer scans. Ophthalmology. 2011;118:763–7.

    PubMed  Article  Google Scholar 

  18. 18.

    Na JH, Sung KR, Baek S, Kim YJ, Durbin MK, Lee HJ, et al. Detection of glaucoma progression by assessment of segmented macular thickness data obtained using spectral domain optical coherence tomography. Invest Ophth Vis Sci. 2012;53:3817–26.

    Article  Google Scholar 

  19. 19.

    Na JH, Sung KR, Lee JR, Lee KS, Baek S, Kim HK, et al. Detection of glaucomatous progression by spectral-domain optical coherence tomography. Ophthalmology. 2013;120:1388–95.

    PubMed  Article  Google Scholar 

  20. 20.

    Cheung CYL, Leung CKS, Lin DS, Pang CP, Lam DSC. Relationship between retinal nerve fiber layer measurement and signal strength in optical coherence tomography. Ophthalmology. 2008;115:1347–51.

    PubMed  Article  Google Scholar 

  21. 21.

    Cheung CY, Chan N, Leung CK. Retinal nerve fiber layer imaging with spectral-domain optical coherence tomography: impact of signal strength on analysis of the RNFL Map. Asia Pac J Ophthalmol (Phila). 2012;1:19–23.

    Article  Google Scholar 

  22. 22.

    Biswas S, Lin C, Leung CK. Evaluation of a myopic normative database for analysis of retinal nerve fiber layer thickness. JAMA Ophthalmol. 2016;134:1032–9.

    PubMed  Article  Google Scholar 

  23. 23.

    Andresen SL. John McCarthy: Father of AI. Ieee Intell Syst. 2002;17:84–5.

    Article  Google Scholar 

  24. 24.

    Simon A, Venkatesan S. An overview of machine learning and its applications. Int J Elec Sci Eng. 2015;1:3

    Google Scholar 

  25. 25.

    Shinde PP, Shah S. A review of machine learning and deep learning applications. In: Proceedings of the 2018 Fourth International Conference on Computing Communication Control and Automation (Iccubea). 2018.

  26. 26.

    Li Z, He Y, Keel S, Meng W, Chang RT, He M. Efficacy of a deep learning system for detecting glaucomatous optic neuropathy based on color fundus photographs. Ophthalmology. 2018;125:1199–206.

    PubMed  Article  Google Scholar 

  27. 27.

    Liu HR, Li L, Wormstone IM, Qiao CY, Zhang C, Liu P, et al. Development and validation of a deep learning system to detect glaucomatous optic neuropathy using fundus photographs. Jama Ophthalmol. 2019;137:1353–60.

    PubMed Central  Article  PubMed  Google Scholar 

  28. 28.

    Rossetto JD, Melo LAS Jr., Campos MS, Tavares IM. Agreement on the evaluation of glaucomatous optic nerve head findings by ophthalmology residents and a glaucoma specialist. Clin Ophthalmol. 2017;11:1281–4.

    PubMed  PubMed Central  Article  Google Scholar 

  29. 29.

    de Boer JF, Cense B, Park BH, Pierce MC, Tearney GJ, Bouma BE. Improved signal-to-noise ratio in spectral-domain compared with time-domain optical coherence tomography. Opt Lett. 2003;28:2067–9.

    PubMed  Article  Google Scholar 

  30. 30.

    Chang RT, Knight OJ, Feuer WJ, Budenz DL. Sensitivity and specificity of time-domain versus spectral-domain optical coherence tomography in diagnosing early to moderate glaucoma. Ophthalmology. 2009;116:2294–9.

    PubMed  Article  Google Scholar 

  31. 31.

    Johnson DE, El-Defrawy SR, Almeida DR, Campbell RJ. Comparison of retinal nerve fibre layer measurements from time domain and spectral domain optical coherence tomography systems. Can J Ophthalmol. 2009;44:562–6.

    PubMed  Article  Google Scholar 

  32. 32.

    Chen TC, Hoguet A, Junk AK, Nouri-Mahdavi K, Radhakrishnan S, Takusagawa HL, et al. Spectral-domain OCT: helping the clinician diagnose glaucoma: a report by the American academy of ophthalmology. Ophthalmology. 2018;125:1817–27.

    PubMed  Article  Google Scholar 

  33. 33.

    Sung KR, Na JH, Lee Y. Glaucoma diagnostic capabilities of optic nerve head parameters as determined by cirrus HD optical coherence tomography. J Glaucoma. 2012;21:498–504.

    PubMed  Article  Google Scholar 

  34. 34.

    Chauhan BC, Burgoyne CF. From clinical examination of the optic disc to clinical assessment of the optic nerve head: a paradigm change. Am J Ophthalmol. 2013;156:218–27 e2.

    PubMed  PubMed Central  Article  Google Scholar 

  35. 35.

    Chauhan BC, O’Leary N, AlMobarak FA, Reis ASC, Yang H, Sharpe GP, et al. Enhanced detection of open-angle glaucoma with an anatomically accurate optical coherence tomography-derived neuroretinal rim parameter. Ophthalmology. 2013;120:535–43.

    PubMed  Article  Google Scholar 

  36. 36.

    Sharma R, Sharma A, Arora T, Sharma S, Sobti A, Jha B, et al. Application of anterior segment optical coherence tomography in glaucoma. Surv Ophthalmol. 2014;59:311–27.

    PubMed  Article  Google Scholar 

  37. 37.

    O’Mahony N, Campbell S, Carvalho A, Harapanahalli S, Hernandez GV, Krpalkova L, et al. Deep learning vs. traditional computer vision. Adv Intell Syst. 2020;943:128–44.

    Google Scholar 

  38. 38.

    Wang JJ, Ma YL, Zhang LB, Gao RX, Wu DZ. Deep learning for smart manufacturing: methods and applications. J Manuf Syst. 2018;48:144–56.

    Article  Google Scholar 

  39. 39.

    Rawat W, Wang Z. Deep convolutional neural networks for image classification: a comprehensive review. Neural Comput. 2017;29:2352–449.

    PubMed  Article  Google Scholar 

  40. 40.

    Aggarwal CC. Convolutional neural network. neural networks and deep learning. Springer, Cham; 2018.

  41. 41.

    LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521:436–44.

    CAS  PubMed  Article  Google Scholar 

  42. 42.

    Benuwa B, Zhan YZ, Ghansah B, Wornyo DK, Kataka FB. A review of deep machine learning. Int J Eng Res Afr. 2016;24:124–36.

    Article  Google Scholar 

  43. 43.

    Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Commun Acm. 2017;60:84–90.

    Article  Google Scholar 

  44. 44.

    Ying X. An overview of overfitting and its solutions. Proceedings of the 2018 International Conference on Computer Information Science and Application Technology. 2019. p. 1168.

  45. 45.

    Aggarwal CC. Teaching deep learners to generalize. Neural networks and deep learning. Springer, Cham; 2018.

  46. 46.

    Yosinski J, Clune J, Bengio Y, Lipson H. How transferable are features in deep neural networks? Adv Neur In. 2014;27:3320–8.

    Google Scholar 

  47. 47.

    Asaoka R, Murata H, Hirasawa K, Fujino Y, Matsuura M, Miki A, et al. Using deep learning and transfer learning to accurately diagnose early-onset glaucoma from macular optical coherence tomography images. Am J Ophthalmol. 2019;198:136–45.

    PubMed  PubMed Central  Article  Google Scholar 

  48. 48.

    Muhammad H, Fuchs TJ, De Cuir N, De Moraes CG, Blumberg DM, Liebmann JM, et al. Hybrid deep learning on single wide-field optical coherence tomography scans accurately classifies glaucoma suspects. J Glaucoma. 2017;26:1086–94.

    PubMed  PubMed Central  Article  Google Scholar 

  49. 49.

    Lee J, Kim YK, Park KH, Jeoung JW. Diagnosing glaucoma with spectral-domain optical coherence tomography using deep learning classifier. J Glaucoma. 2020;29:287–94.

    PubMed  Article  Google Scholar 

  50. 50.

    Thompson AC, Jammal AA, Berchuck SI, Mariottoni EB, Medeiros FA. Assessment of a segmentation-free deep learning algorithm for diagnosing glaucoma from optical coherence tomography scans. JAMA Ophthalmol. 2020;138:333–9.

    PubMed  Article  Google Scholar 

  51. 51.

    Wang X, Chen H, Ran AR, Luo LY, Chan PP, Tham CC, et al. Towards multi-center glaucoma OCT image screening with semi-supervised joint structure and function multi-task learning. Med Image Anal. 2020;63:101695.

    PubMed  Article  Google Scholar 

  52. 52.

    Maetschke S, Antony B, Ishikawa H, Wollstein G, Schuman J, Garnavi R. A feature agnostic approach for glaucoma detection in OCT volumes. Plos ONE. 2019;14:e0219126.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  53. 53.

    Ran AR, Cheung CY, Wang X, Chen H, Luo L-y, Chan PP, et al. Detection of glaucomatous optic neuropathy with spectral-domain optical coherence tomography: a retrospective training and validation deep-learning analysis. Lancet Digital Health. 2019;1:e172–e82.

    PubMed  Article  Google Scholar 

  54. 54.

    Russakoff DB, Mannil SS, Oakley JD, Ran AR, Cheung CY, Dasari S, et al. A 3D deep learning system for detecting referable glaucoma using full OCT macular cube scans. Transl Vis Sci Techn. 2020;9:12.

    Article  Google Scholar 

  55. 55.

    Medeiros FA, Jammal AA, Thompson AC. From machine to machine: an OCT-trained deep learning algorithm for objective quantification of glaucomatous damage in fundus photographs. Ophthalmology. 2019;126:513–21.

    PubMed  Article  Google Scholar 

  56. 56.

    Thompson AC, Jammal AA, Medeiros FA. A deep learning algorithm to quantify neuroretinal rim loss from optic disc photographs. Am J Ophthalmol. 2019;201:9–18.

    PubMed  PubMed Central  Article  Google Scholar 

  57. 57.

    European Glaucoma Society Terminology and Guidelines for Glaucoma, 4th Edition - Chapter 2: Classification and terminologySupported by the EGS Foundation: Part 1: Foreword; Introduction; Glossary; Chapter 2 Classification and Terminology. Br J Ophthalmol. 2017;101:73–127.

  58. 58.

    Fu H, Baskaran M, Xu Y, Lin S, Wong DWK, Liu J, et al. A deep learning system for automated angle-closure detection in anterior segment optical coherence tomography images. Am J Ophthalmol. 2019;203:37–45.

    PubMed  Article  Google Scholar 

  59. 59.

    Fu H, Xu Y, Lin S, Wong DWK, Baskaran M, Mahesh M, et al. Angle-closure detection in anterior segment OCT based on multilevel deep network. IEEE Trans Cybern. 2019;50:3358–66.

    PubMed  Article  Google Scholar 

  60. 60.

    Xu BY, Chiang M, Chaudhary S, Kulkarni S, Pardeshi AA, Varma R. Deep learning classifiers for automated detection of gonioscopic angle closure based on anterior segment OCT images. Am J Ophthalmol. 2019;208:273–80.

    PubMed  PubMed Central  Article  Google Scholar 

  61. 61.

    Hao H, Zhao Y, Fu H, Shang Q, Li F, Zhang X, et al. Anterior chamber angles classification in anterior segment OCT images via multi-scale regions convolutional neural networks. Conf Proc IEEE Eng Med Biol Soc. 2019;2019:849–52.

    Google Scholar 

  62. 62.

    Badano A, Graff CG, Badal A, Sharma D, Zeng RP, Samuelson FW, et al. Evaluation of digital breast tomosynthesis as replacement of full-field digital mammography using an in silico imaging trial. Jama Netw Open. 2018;1:e185474.

    PubMed  PubMed Central  Article  Google Scholar 

  63. 63.

    Cha KH, Petrick N, Pezeshk A, Graff CG, Sharma D, Badal A, et al. Reducing overfitting of a deep learning breast mass detection algorithm in mammography using synthetic images. Med Imag. 2019;10950.

  64. 64.

    Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, et al. Generative Adversarial Nets. Adv Neur In. 2014;27:2672–80.

    Google Scholar 

  65. 65.

    Sun Y, Zhou C, Fu Y, Xue X. Parasitic GAN for Semi-Supervised Brain Tumor Segmentation, 2019 IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan, 2019, pp. 1535–1539, https://doi.org/10.1109/ICIP.2019.8803073.

  66. 66.

    Yang Y, Nan FT, Yang P, Meng Q, Xie YF, Zhang DH, et al. GAN-based semi-supervised learning approach for clinical decision support in health-IoT platform. IEEE Access. 2019;7:8048–57.

    Article  Google Scholar 

  67. 67.

    Wang X, Tang F, Chen H, Luo L, Tang Z, Ran AR, et al. UD-MIL: uncertainty-driven deep multiple instance learning for OCT image classification. IEEE J Biomed Health Inform. 2020. https://doi.org/10.1109/JBHI.2020.2983730.

  68. 68.

    Chen C, Dou Q, Chen H, Qin J, Heng PA. Synergistic image and feature adaptation: towards cross-modality domain adaptation for medical image segmentation. in: Thirty-Third Aaai Conference on Artificial Intelligence / Thirty-First Innovative Applications of Artificial Intelligence Conference / Ninth Aaai Symposium on Educational Advances in Artificial Intelligence. 2019:865–72.

  69. 69.

    Xie L, Yang S, Squirrell D, Vaghefi E. Towards implementation of AI in New Zealand national diabetic screening program: cloud-based, robust, and bespoke. Plos One. 2020;15:e0225015.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  70. 70.

    Tan NYQ, Friedman DS, Stalmans I, Ahmed IIK, Sng CCA. Glaucoma screening: where are we and where do we need to go? Current Opin Ophthalmol. 2020;31:91–100.

    Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Carol Y. Cheung.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

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

Verify currency and authenticity via CrossMark

Cite this article

Ran, A.R., Tham, C.C., Chan, P.P. et al. Deep learning in glaucoma with optical coherence tomography: a review. Eye 35, 188–201 (2021). https://doi.org/10.1038/s41433-020-01191-5

Download citation

Further reading

Search

Quick links