Skip to main content
SpringerLink
Log in

AI and Glaucoma

  • Chapter
  • First Online:
Artificial Intelligence in Ophthalmology

  • 867 Accesses

Abstract

Recent artificial intelligence advances in ophthalmology, especially deep learning (DL), have shown enormous potential with vast quantities of data. Unlike diabetic retinopathy and age-related macular degeneration where early DL initiatives in ophthalmology focused on, there have been limited but expanding efforts for utilizing DL algorithms to improve diagnosis and management of glaucoma. In this chapter, we provide a summary of current AI applications, challenges, and state-of-the-art DL systems in glaucoma. Key applications involve glaucoma diagnosis, longitudinal progression analysis, structural-functional correlation investigation, scan enhancement, and new knowledge discovery. Given the multifactorial etiology of glaucoma, various input modalities such as fundus photographs, optical coherence tomography (OCT), visual field testing, and demographic data have been investigated along with DL models. The ability of extracting meaningful representations from high dimensional and complex multi-modal data enables DL system to achieve high accuracy of glaucoma diagnosis and prognosis and to discover new knowledges to improve our current understanding of glaucoma. Though DL algorithms have shown promising improvements in performing tasks related to glaucoma, lack of large dataset, diversity of data formations and evaluation criteria, and poor model interpretability still remain as challenges to the research community.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Tan O, Chopra V, Lu AT, et al. Detection of macular ganglion cell loss in glaucoma by Fourier-domain optical coherence tomography. Ophthalmology. 2009;116(12):2305–2314.e1–e2.

    Article  PubMed  Google Scholar 

  2. Quigley HA, Broman AT. The number of people with glaucoma worldwide in 2010 and 2020. Br J Ophthalmol. 2006;90(3):262–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Ramulu P. Glaucoma and disability: which tasks are affected, and at what stage of disease? Curr Opin Ophthalmol. 2009;20:92.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Hood DC, Tsamis E, Bommakanti NK, Joiner DB, Al-Aswad LA, Blumberg DM, et al. Structure-function agreement is better than commonly thought in eyes with early glaucoma. Invest Ophthalmol Vis Sci. 2019;60(13):4241–8.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Rao HL, Zangwill LM, Weinreb RN, Leite MT, Sample PA, Medeiros FA. Structure-function relationship in glaucoma using spectral-domain optical coherence tomography. Arch Ophthalmol. 2011;129(7):864–71.

    Article  PubMed  Google Scholar 

  6. Leite MT, Zangwill LM, Weinreb RN, Rao HL, Alencar LM, Medeiros FA. Structure-function relationships using the Cirrus spectral domain optical coherence tomograph and standard automated perimetry. J Glaucoma. 2012;21(1):49.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Wollstein G, Kagemann L, Bilonick RA, Ishikawa H, Folio LS, Gabriele ML, et al. Retinal nerve fibre layer and visual function loss in glaucoma: the tipping point. Br J Ophthalmol. 2012;96(1):47–52.

    Article  PubMed  Google Scholar 

  8. Malik R, Swanson WH, Garway-Heath DF. Structure–function relationship in glaucoma: past thinking and current concepts. Clin Exp Ophthalmol. 2012;40(4):369–80.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Harwerth RS, Wheat JL, Fredette MJ, Anderson DR. Linking structure and function in glaucoma. Prog Retin Eye Res. 2010;29(4):249–71.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Garg A, Hood DC, Pensec N, Liebmann JM, Blumberg DM. Macular damage, as determined by structure-function staging, is associated with worse vision-related quality of life in early glaucoma. Am J Ophthalmol. 2018;194:88–94.

    Article  PubMed  Google Scholar 

  11. Taylor P, Kalpathy-Cramer J. Machine learning has arrived! Aaron Lee, MD, MSCI-Seattle, Washington.

    Google Scholar 

  12. Rahimy E. Deep learning applications in ophthalmology. Curr Opin Ophthalmol. 2018;29(3):254–60.

    Article  PubMed  Google Scholar 

  13. Ting DSW, Pasquale LR, Peng L, Campbell JP, Lee AY, Raman R, et al. Artificial intelligence and deep learning in ophthalmology. Br J Ophthalmol. 2019;103(2):167–75.

    Article  PubMed  Google Scholar 

  14. Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316(22):2402–10.

    Article  PubMed  Google Scholar 

  15. Chen X, Xu Y, Wong DWK, Wong TY, Liu J. Glaucoma detection based on deep convolutional neural network. In: 2015 37th annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE; 2015. p. 715–8.

    Chapter  Google Scholar 

  16. Thakur N, Juneja M. Survey on segmentation and classification approaches of optic cup and optic disc for diagnosis of glaucoma. Biomed Signal Process Control. 2018;42:162–89.

    Article  Google Scholar 

  17. Shankaranarayana M, Ram SM, Mitra K, Sivaprakasam K. Joint optic disc and cup segmentation using fully convolutional and adversarial networks. In: Fetal, infant and ophthalmic medical image analysis, vol. 10554. Cham: Springer; 2017. p. 168–76.

    Chapter  Google Scholar 

  18. Zilly J, Buhmann JM, Mahapatra D. Glaucoma detection using entropy sampling and ensemble learning for automatic optic cup and disc segmentation. Comput Med Imaging Graphics. 2017;55:28–41.

    Article  Google Scholar 

  19. Sevastopolsky A. Optic disc and cup segmentation methods for glaucoma detection with modification of U-Net convolutional neural network. Pattern Recognit Image Anal. 2017;27(3):618–24.

    Article  Google Scholar 

  20. Fu H, Cheng J, Xu Y, Wong DWK, Liu J, Cao X. Joint optic disc and cup segmentation based on multi-label deep network and polar transformation. IEEE Trans Med Imaging. 2018:1–9.

    Google Scholar 

  21. Al-Bander B, Zheng Y. Dense fully convolutional segmentation of the optic disc and cup in colour fundus for glaucoma diagnosis. Symmetry. 2018;10(4):87.

    Article  Google Scholar 

  22. Fu H, Cheng J, Xu Y, Zhang C, Wong DWK, Liu J, Cao X. Disc-aware ensemble network for glaucoma screening from fundus image. IEEE Trans Med Imaging. 2018;37(11):2493–501.

    Article  PubMed  Google Scholar 

  23. Zhixi L, He Y, Keel S, Meng W, Chang R, He M. Efficacy of a deep learning system for detecting glaucomatous optic neuropathy based on color fundus photographs. Ophthalmology. 2018;125(8):1199–206.

    Article  Google Scholar 

  24. Chakravarty A, Sivswamy J. A deep learning based joint segmentation and classification framework for glaucoma assessment in retinal color fundus images. arXiv preprint arXiv:1808.01355.

    Google Scholar 

  25. Chai Y, Liu H, Xu J. Glaucoma diagnosis based on both hidden features and domain knowledge through deep learning models. Knowl-Based Syst. 2018;161:147–56.

    Article  Google Scholar 

  26. Asaoka R, Murata H, Iwase A, Araie M. Detecting preperimetric glaucoma with standard automated perimetry using a deep learning classifier. Ophthalmology. 2016;123(9):1974–80.

    Article  PubMed  Google Scholar 

  27. Sample PA, Chan K, Boden C, Lee TW, Blumenthal EZ, Weinreb RN, et al. Using unsupervised learning with variational bayesian mixture of factor analysis to identify patterns of glaucomatous visual field defects. Invest Ophthalmol Vis Sci. 2004;45(8):2596–605.

    Article  PubMed  Google Scholar 

  28. Kucur ŞS, Holló G, Sznitman R. A deep learning approach to automatic detection of early glaucoma from visual fields. PLoS One. 2018;13(11):e0206081.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  29. Li F, Wang Z, Qu G, Song D, Yuan Y, Xu Y, et al. Automatic differentiation of Glaucoma visual field from non-glaucoma visual filed using deep convolutional neural network. BMC Med Imaging. 2018;18(1):35.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Muhammad H, Fuchs T, De Cuir N, De Moraes C, Blumberg D, Liebmann J, Ritch R, Hood D. Hybrid deep learning on single wide-field optical coherence tomography scans accurately classifies glaucoma suspects. J Glaucoma. 2017;26(12):1086–94.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Fu H, Xu Y, Lin S, Wong D, Mani B, Mahesh M, Aung T, Liu J. Multi-context deep network for angle-closure glaucoma screening in anterior segment oct. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer; 2018. p. 356–63.

    Google Scholar 

  32. 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(7):e0219126.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Thakoor KA, Li X, Tsamis E, Sajda P, Hood DC. Enhancing the accuracy of glaucoma detection from OCT probability maps using convolutional neural networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE; 2019. p. 2036–40.

    Chapter  Google Scholar 

  34. Park K, Kim J, Lee J. Visual field prediction using recurrent neural network. Sci Rep. 2019;9(1):1–12.

    Google Scholar 

  35. Sedai S, Antony B, Ishikawa H, Wollstein G, Schuman JS, Garnavi R. Forecasting retinal nerve fiber layer thickness from multimodal temporal data incorporating OCT volumes. Ophthalmol Glaucoma. 2020;3(1):14–24.

    Article  PubMed  Google Scholar 

  36. Chen Z, Wang Y, Wollstein G, de los Angeles Ramos-Cadena M, Schuman J, Ishikawa H. Macular GCIPL thickness map prediction via time-aware convolutional LSTM. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI). IEEE; 2020. p. 1–5.

    Google Scholar 

  37. Uesaka T, Morino K, Sugiura H, Kiwaki T, Murata H, Asaoka R, Yamanishi K. Multi-view learning over retinal thickness and visual sensitivity on glaucomatous eyes. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2017. p. 2041–50.

    Google Scholar 

  38. Sugiura H, Kiwaki T, Yousefi S, Murata H, Asaoka R, Yamanishi K. Estimating glaucomatous visual sensitivity from retinal thickness with pattern-based regularization and visualization. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2018. p. 783–92.

    Google Scholar 

  39. Christopher M, Bowd C, Belghith A, Goldbaum MH, Weinreb RN, Fazio MA, et al. Deep learning approaches predict glaucomatous visual field damage from OCT optic nerve head En face images and retinal nerve fiber layer thickness maps. Ophthalmology. 2020;127(3):346–56.

    Article  PubMed  Google Scholar 

  40. Maetschke S, Antony B, Ishikawa H, Wollstein G, Schuman J, Garnavi R. Inference of visual field test performance from OCT volumes using deep learning. arXiv preprint arXiv:1908.01428. 2019.

    Google Scholar 

  41. Christopher M, Proudfoot JA, Bowd C, Belghith A, Goldbaum MH, Rezapour J, et al. Deep learning models based on unsegmented OCT RNFL circle scans provide accurate detection of glaucoma and high resolution prediction of visual field damage. Invest Ophthalmol Vis Sci. 2020;61(7):1439.

    Google Scholar 

  42. Mendoza L, Christopher M, Belghith A, Bowd C, Rezapour J, Fazio MA, et al. Deep learning models predict age, sex and race from OCT optic nerve head circle scans. Invest Ophthalmol Vis Sci. 2020;61(7):2012.

    Google Scholar 

  43. Halupka KJ, Antony BJ, Lee MH, Lucy KA, Rai RS, Ishikawa H, et al. Retinal optical coherence tomography image enhancement via deep learning. Biomed Optics Express. 2018;9(12):6205–21.

    Article  Google Scholar 

  44. Zeiler MD, Fergus R. Visualizing and understanding convolutional networks. In: European conference on computer vision. Cham: Springer; 2014. p. 818–33.

    Google Scholar 

  45. Simonyan K, Vedaldi A, Zisserman A. Deep inside convolutional networks: visualising image classification models and saliency maps. arXiv preprint arXiv:1312.6034. 2013.

    Google Scholar 

  46. Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A. Learning deep features for discriminative localization. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2016. p. 2921–9.

    Google Scholar 

  47. Bowd C, Chan K, Zangwill LM, et al. Comparing neural networks and linear discriminant functions for glaucoma detection using confocal scanning laser ophthalmoscopy of the optic disc. Invest Ophthalmol Vis Sci. 2002;43:3444–54.

    PubMed  Google Scholar 

  48. Goldbaum MH, Sample PA, White H, Colt B, Raphaelian P, Fechtner RD, Weinreb RN. Interpretation of automated perimetry for glaucoma by neural network. Invest Ophthalmol Vis Sci. 1994;35(9):3362–73.

    CAS  PubMed  Google Scholar 

  49. Chan K, Lee TW, Sample PA, Goldbaum MH, Weinreb RN, Sejnowski TJ. Comparison of machine learning and traditional classifiers in glaucoma diagnosis. IEEE Trans Biomed Eng. 2002;49(9):963–74.

    Article  PubMed  Google Scholar 

  50. Goldbaum MH, Sample PA, Chan K, Williams J, Lee TW, Blumenthal E, et al. Comparing machine learning classifiers for diagnosing glaucoma from standard automated perimetry. Invest Ophthalmol Vis Sci. 2002;43(1):162–9.

    PubMed  Google Scholar 

  51. Aurenhammer F. Voronoi diagrams—a survey of a fundamental geometric data structure. ACM Computing Surveys (CSUR). 1991;23(3):345–405.

    Article  Google Scholar 

  52. Townsend KA, Wollstein G, Danks D, et al. Heidelberg retina tomograph 3 machine learning classifiers for glaucoma detection. Br J Ophthalmol. 2008;92:814–8. https://doi.org/10.1136/bjo.2007.133074.

    Article  CAS  PubMed  Google Scholar 

  53. Zangwill LM, Chan K, Bowd C, et al. Heidelberg retina tomograph measurements of the optic disc and parapapillary retina for detecting glaucoma analyzed by machine learning classifiers. Invest Ophthalmol Vis Sci. 2004;45:3144–51. https://doi.org/10.1167/iovs.04-0202.

    Article  PubMed  Google Scholar 

  54. Uchida H, Brigatti L, Caprioli J. Detection of structural damage from glaucoma with confocal laser image analysis. Invest Ophthalmol Vis Sci. 1996;37:2393–401.

    CAS  PubMed  Google Scholar 

  55. Adler W, Peters A, Lausen B. Comparison of classifiers applied to confocal scanning laser ophthalmoscopy data. Methods Inf Med. 2008;47:38–46. https://doi.org/10.3414/ME0348.

    Article  CAS  PubMed  Google Scholar 

  56. Bowd C, Zangwill LM, Medeiros FA, et al. Confocal scanning laser ophthalmoscopy classifiers and stereophotograph evaluation for prediction of visual field abnormalities in glaucoma-suspect eyes. Invest Ophthalmol Vis Sci. 2004;45:2255–62.

    Article  PubMed  Google Scholar 

  57. Weinreb RN, Zangwill L, Berry CC, et al. Detection of glaucoma with scanning laser polarimetry. Arch Ophthalmol. 1998;116:1583–9. https://doi.org/10.1001/archopht.116.12.1583.

    Article  CAS  PubMed  Google Scholar 

  58. Bowd C, Medeiros FA, Zhang Z, et al. Relevance vector machine and support vector machine classifier analysis of scanning laser polarimetry retinal nerve fiber layer measurements. Invest Ophthalmol Vis Sci. 2005;46:1322–9. https://doi.org/10.1167/iovs.04-1122.

    Article  PubMed  Google Scholar 

  59. Zhang Z, Yin FS, Liu J, Wong WK, Tan NM, Lee BH, et al. Origa-light: an online retinal fundus image database for glaucoma analysis and research. In: 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology. IEEE; 2010. p. 3065–8.

    Google Scholar 

  60. Sng CC, Foo LL, Cheng CY, Allen JC, He M, Krishnaswamy G, Nongpiur ME, Friedman DS, Wong TY, Aung T. Determinants of anterior chamber depth: the Singapore Chinese Eye Study. Opthalmology. 2012;119(6):1143–50.

    Article  Google Scholar 

  61. Chen X, Xu Y, Yan S, Wong DWK, Wong TY, Liu J. Automatic feature learning for glaucoma detection based on deep learning. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer; 2015. p. 669–77.

    Google Scholar 

  62. He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2016. p. 770–8.

    Google Scholar 

  63. Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention. Cham: Springer; 2015. p. 234–41.

    Google Scholar 

  64. Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2016. p. 2818–26.

    Google Scholar 

  65. Ren S, He K, Girshick R, Sun J. Faster r-cnn: towards real-time object detection with region proposal networks. In: Advances in neural information processing systems. 2015. p. 91–9.

    Google Scholar 

  66. Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2015. p. 3431–40.

    Google Scholar 

  67. Bizios D, Heijl A, Hougaard JL, Bengtsson B. Machine learning classifiers for glaucoma diagnosis based on classification of retinal nerve fibre layer thickness parameters measured by Stratus OCT. Acta Ophthalmol. 2010;88(1):44–52.

    Article  PubMed  Google Scholar 

  68. Barella KA, Costa VP, Gonçalves Vidotti V, Silva FR, Dias M, Gomi ES. Glaucoma diagnostic accuracy of machine learning classifiers using retinal nerve fiber layer and optic nerve data from SD-OCT. J Ophthalmol. 2013.

    Google Scholar 

  69. Kotsiantis SB, Zaharakis I, Pintelas P. Supervised machine learning: a review of classification techniques. In: Maglogiannis I, et al., editors. Emerging Artificial Intelligence Applications in Computer Engineering. IOS Press; 2007. p. 3–24.

    Google Scholar 

  70. Brigatti L, Hoffman D, Caprioli J. Neural networks to identify glaucoma with structural and functional measurements. Am J Ophthalmol. 1996;121:511–21.

    Article  CAS  PubMed  Google Scholar 

  71. Bowd C, Hao J, Tavares IM, et al. Bayesian machine learning classifiers for combining structural and functional measurements to classify healthy and glaucomatous eyes. Invest Ophthalmol Vis Sci. 2008;49:945–53.

    Article  PubMed  Google Scholar 

  72. Leung CKS, Cheung CYL, Weinreb RN, Qiu K, Liu S, Li H, et al. Evaluation of retinal nerve fiber layer progression in glaucoma: a study on optical coherence tomography guided progression analysis. Invest Ophthalmol Vis Sci. 2010;51(1):217–22.

    Article  PubMed  Google Scholar 

  73. Na JH, Sung KR, Baek S, Lee JY, Kim S. Progression of retinal nerve fiber layer thinning in glaucoma assessed by cirrus optical coherence tomography-guided progression analysis. Curr Eye Res. 2013;38(3):386–95.

    Article  PubMed  Google Scholar 

  74. Caprioli J, Mock D, Bitrian E, Afifi AA, Yu F, Nouri-Mahdavi K, Coleman AL. A method to measure and predict rates of regional visual field decay in glaucoma. Invest Ophthalmol Vis Sci. 2011;52(7):4765–73.

    Article  PubMed  Google Scholar 

  75. Otarola F, Chen A, Morales E, Yu F, Afifi A, Caprioli J. Course of glaucomatous visual field loss across the entire perimetric range. JAMA ophthalmology. 2016;134(5):496–502.

    Article  PubMed  Google Scholar 

  76. Chen A, Nouri-Mahdavi K, Otarola FJ, Yu F, Afifi AA, Caprioli J. Models of glaucomatous visual field loss. Invest Ophthalmol Vis Sci. 2014;55(12):7881–7.

    Article  PubMed  Google Scholar 

  77. Warren JL, Mwanza JC, Tanna AP, Budenz DL. A statistical model to analyze clinician expert consensus on glaucoma progression using spatially correlated visual field data. Transl Vis Sci Technol. 2016;5(4):14.

    Article  PubMed  PubMed Central  Google Scholar 

  78. Betz-Stablein BD, Morgan WH, House PH, Hazelton ML. Spatial modeling of visual field data for assessing glaucoma progression. Invest Ophthalmol Vis Sci. 2013;54(2):1544–53.

    Article  PubMed  Google Scholar 

  79. Anderson AJ. Comparison of three parametric models for glaucomatous visual field progression rate distributions. Transl Vis Sci Technol. 2015;4(4):2–2.

    Article  PubMed  PubMed Central  Google Scholar 

  80. VanBuren J, Oleson JJ, Zamba GK, Wall M. Integrating independent spatio-temporal replications to assess population trends in disease spread. Stat Med. 2016;35(28):5210–21.

    Article  PubMed  Google Scholar 

  81. Wen JC, Lee CS, Keane PA, Xiao S, Rokem AS, Chen PP, et al. Forecasting future Humphrey visual fields using deep learning. PLoS One. 2019;14(4):e0214875.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Song Y, Ishikawa H, Wu M, Liu YY, Lucy KA, Lavinsky F, et al. Clinical prediction performance of glaucoma progression using a 2-dimensional continuous-time hidden markov model with structural and functional measurements. Ophthalmology. 2018;125(9):1354–61.

    Article  PubMed  Google Scholar 

  83. Hood DC, Kardon RH. A framework for comparing structural and functional measures of glaucomatous damage. Prog Retin Eye Res. 2007;26(6):688–710.

    Article  PubMed  PubMed Central  Google Scholar 

  84. Zhu H, Crabb DP, Schlottmann PG, Lemij HG, Reus NJ, Healey PR, et al. Predicting visual function from the measurements of retinal nerve fiber layer structure. Invest Ophthalmol Vis Sci. 2010;51(11):5657–66.

    Article  PubMed  Google Scholar 

  85. Russell RA, Malik R, Chauhan BC, Crabb DP, Garway-Heath DF. Improved estimates of visual field progression using Bayesian linear regression to integrate structural information in patients with ocular hypertension. Invest Ophthalmol Vis Sci. 2012;53(6):2760–9.

    Article  PubMed  PubMed Central  Google Scholar 

  86. Pollet-Villard F, Chiquet C, Romanet JP, Noel C, Aptel F. Structure-function relationships with spectral-domain optical coherence tomography retinal nerve fiber layer and optic nerve head measurements. Invest Ophthalmol Vis Sci. 2014;55(5):2953–62.

    Article  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, New York University, Brooklyn, NY, USA

    Zhiqi Chen & Joel S. Schuman

  2. Department of Ophthalmology, NYU Langone Health, New York, NY, USA

    Zhiqi Chen, Gadi Wollstein, Joel S. Schuman & Hiroshi Ishikawa

  3. Department of Biomedical Engineering, New York University, Brooklyn, NY, USA

    Gadi Wollstein, Joel S. Schuman & Hiroshi Ishikawa

Authors
  1. Zhiqi Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gadi Wollstein
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Joel S. Schuman
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hiroshi Ishikawa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hiroshi Ishikawa .

Editor information

Editors and Affiliations

  1. Department of Ophthalmology, University of Warmia and Mazury, Olsztyn, Poland

    Andrzej Grzybowski

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Chen, Z., Wollstein, G., Schuman, J.S., Ishikawa, H. (2021). AI and Glaucoma. In: Grzybowski, A. (eds) Artificial Intelligence in Ophthalmology. Springer, Cham. https://doi.org/10.1007/978-3-030-78601-4_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-78601-4_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-78600-7

  • Online ISBN: 978-3-030-78601-4

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics

Search

Navigation