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Yousefi S. Clinical Applications of Artificial Intelligence in Glaucoma. JOVR [Internet]. 2023Feb.13 [cited 2024Jun.2];18(1):97–112. Available from: https://knepublishing.com/index.php/JOVR/article/view/12730
https://doi.org/10.18502/jovr.v18i1.12730
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Siamak Yousefi
Siamak Yousefi
vDepartment of Ophthalmology, University of Tennessee Health Science Center, Memphis, TN, USA
Published Date
- Feb 13, 2023
Clinical Applications of Artificial Intelligence in Glaucoma
Abstract
Ophthalmology is one of the major imaging-intensive fields of medicine and thus has potential for extensive applications of artificial intelligence (AI) to advance diagnosis, drug efficacy, and other treatment-related aspects of ocular disease. AI has made impressive progress in ophthalmology within the past few years and two autonomous AIenabled systems have received US regulatory approvals for autonomously screening for mid-level or advanced diabetic retinopathy and macular edema. While no autonomous AI-enabled system for glaucoma screening has yet received US regulatory approval, numerous assistive AI-enabled software tools are already employed in commercialized instruments for quantifying retinal images and visual fields to augment glaucoma research and clinical practice. In this literature review (non-systematic), we provide an overview of AI applications in glaucoma, and highlight some limitations and considerations for AI integration and adoption into clinical practice.
Keywords:
Artificial Intelligence, Convolutional Neural Network (CNN), Deep Learning, Glaucoma, Machine Learning, Ophthalmology
References
1. Ting DS, Cheung GC, Wong TY. Diabetic retinopathy: Global prevalence, major risk factors, screening practices and public health challenges: A review. Clin Exp Ophthalmol 2016;44:260–277.
2. 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;316:2402–2410.
3. De Fauw J, Ledsam JR, Romera-Paredes B, Nikolov S, Tomasev N, Blackwell S, et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat Med 2018;24:1342–1350.
4. Kelman SE, Perell HF, D’Autrechy L, Scott RJ. A neural network can differentiate glaucoma and optic neuropathy visual fields through pattern recognition. In: Mills RP, Heijl A, editors. Perimetry Update 1990/1991, Proceedings of the IXth International Perimetric Society Meeting. Amsterdam/New York: Kugler Publications; 1991. p. 291– 295.
5. Nagata S, Kani K, Sugiyama A. A computer assisted visual field diagnosis system using neural netowrks. In: Mills RP, Heijl A, editors. Perimetry Update 1990/1991, Proceedings of the IXth International Perimetric Society Meeting. Amsterdam/New York: Kugler Publications; 1991. p. 291–295.
6. Mendels F, Heneghan C, Thiran J-P. Identification of the optic disk boundary in retinal images using active contours. 1999.
7. 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;49:963–974.
8. Bengtsson B, Bizios D, Heijl A. Effects of input data on the performance of a neural network in distinguishing normal and glaucomatous visual fields. Invest Ophthalmol Vis Sci 2005;46:3730–3736. 9. Bizios D, Heijl A, Bengtsson B. Trained artificial neural network for glaucoma diagnosis using visual field data: A comparison with conventional algorithms. J Glaucoma 2007;16:20–28.
10. Wroblewski D, Francis B, Chopra V, Kawji AS, Quiros P, Dustin L, et al. Glaucoma detection and evaluation through pattern recognition in standard automated perimetry data. Graefes Arch Clin Exp Ophthalmol 2009;247:1517–1530.
11. Chrastek R, Wolf M, Donath K, Niemann H, Paulus D, Hothorn T, et al. Automated segmentation of the optic nerve head for diagnosis of glaucoma. Med Image Anal 2005;9:297–314.
12. Lim G, Cheng Y, Hsu W, Lee ML. Integrated optic disc and cup segmentation with deep learning. Paper presented at: 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI); 2015 Nov 9-11 Nov.
13. Lee CS, Tyring AJ, Deruyter NP, Wu Y, Rokem A, Lee AY. Deep-learning based, automated segmentation of macular edema in optical coherence tomography. Biomed Opt Express 2017;8:3440–3448.
14. Kugelman J, Alonso-Caneiro D, Read SA, Vincent SJ, Collins MJ. Automatic segmentation of OCT retinal boundaries using recurrent neural networks and graph search. Biomed Opt Express 2018;9:5759–5777.
15. Masood S, Fang R, Li P, Li H, Sheng B, Mathavan A, et al. Automatic choroid layer segmentation from optical coherence tomography images using deep learning. Sci Rep 2019;9:3058.
16. Zhang H, Yang J, Zhou K, Li F, Hu Y, Zhao Y, et al. Automatic segmentation and visualization of choroid in OCT with knowledge infused deep learning. IEEE J Biomed Health Inform 2020;24:3408–3420.
17. Wu Q, Zhang B, Hu Y, Liu B, Cao D, Yang D, et al. Detection of morphologic patterns of diabetic macular edema using a deep learning approach based on optical coherence tomography images. Retina 2020.
18. 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;198:136–145.
19. Lee J, Kim YK, Park KH, Jeoung JW. Diagnosing glaucoma with spectral-domain optical coherence tomography using deep learning classifier. J Glaucoma 2020;29:287–294.
20. Ran AR, Cheung CY, Wang X, Chen H, Luo LY, Chan PP, et al. Detection of glaucomatous optic neuropathy with spectral-domain optical coherence tomography: A retrospective training and validation deep-learning analysis. Lancet Digit Health 2019;1:e172–e182.
21. 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;138:333–339.
22. 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. Investig Ophthalmol Vis Sci 2004;45:2596–2605.
23. Goldbaum MH, Sample PA, Zhang Z, Chan K, Hao J, Lee TW, et al. Using unsupervised learning with independent component analysis to identify patterns of glaucomatous visual field defects. Investig Ophthalmol Vis Sci 2005;46:3676–3683.
24. Bowd C, Weinreb RN, Balasubramanian M, Lee I, Jang G, Yousefi S, et al.Glaucomatous patterns in frequency doubling technology (FDT) perimetry data identified by unsupervised machine learning classifiers. PLoS One 2014;9:e85941
25. Yousefi S, Goldbaum MH, Balasubramanian M, Medeiros FA, Zangwill LM, Liebmann JM, et al. Learning from data: Recognizing glaucomatous defect patterns and detecting progression from visual field measurements. IEEE Trans Biomed Eng 2014;61:2112–2124.
26. Yousefi S, Goldbaum MH, Zangwill LM, Medeiros FA, Bowd C. Recognizing patterns of visual field loss using unsupervised machine learning. Proc SPIE Int Soc Opt Eng 2014;2014:90342M.
27. Elze T, Pasquale LR, Shen LQ, Chen TC, Wiggs JL, Bex PJ. Patterns of functional vision loss in glaucoma determined with archetypal analysis. J R Soc Interface 2015;12:20141118.
28. Yousefi S, Balasubramanian M, Goldbaum MH, Medeiros FA, Zangwill LM, Weinreb RN, et al. Unsupervised Gaussian mixture-model with expectation maximization for detecting glaucomatous progression in standard automated perimetry visual fields. Transl Vis Sci Technol 2016;5:2.
29. Wang M, Shen LQ, Pasquale LR, Boland MV, Wellik SR, De Moraes CG, et al. Artificial intelligence classification of central visual field patterns in glaucoma. Ophthalmology.
30. Thakur A, Goldbaum M, Yousefi S. Convex representations using deep archetypal analysis for predicting glaucoma. IEEE J Transl Eng Health Med 2020;8:3800107.
31. Gupta K, Thakur A, Goldbaum M, Yousefi S. Glaucoma precognition: Recognizing preclinical visual functional signs of glaucoma. Paper presented at: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW); 2020 June 14–19.
32. Nadler Z, Wollstein G, Ishikawa H, Schuman JS. Clinical application of ocular imaging. Optom Vis Sci 2012;89:E543–E553.
33. Stein JD, Talwar N, Laverne AM, Nan B, Lichter PR. Trends in use of ancillary glaucoma tests for patients with open-angle glaucoma from 2001 to 2009. Ophthalmology 2012;119:748–758.
34. Alencar LM, Medeiros FA. The role of standard automated perimetry and newer functional methods for glaucoma diagnosis and follow-up. Indian J Ophthalmol 2011;59:S53–S58.
35. Bengtsson B, Heijl A. A visual field index for calculation of glaucoma rate of progression. Am J Ophthalmol 2008;145:343–353.
36. Phene S, Dunn RC, Hammel N, Liu Y, Krause J, Kitade N, et al. Deep learning and glaucoma specialists: The relative importance of optic disc features to predict glaucoma referral in fundus photographs. Ophthalmology 2019;126:1627–1639.
37. Bussel, II, Wollstein G, Schuman JS. OCT for glaucoma diagnosis, screening and detection of glaucoma progression. Br J Ophthalmol 2014;98:ii15–19.
38. Brusini P, Johnson CA. Staging functional damage in glaucoma: Review of different classification methods. Surv Ophthalmol 2007;52:156–179.
39. Hoover A, Goldbaum M. Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels. IEEE Trans Med Imaging 2003;22:951–958.
40. Wong DW, Liu J, Lim JH, Tan NM, Zhang Z, Lu S, et al. Intelligent fusion of cup-to-disc ratio determination methods for glaucoma detection in ARGALI. Annu Int Conf IEEE Eng Med Biol Soc 2009;2009:5777–5780.
41. Aquino A, Gegundez-Arias ME, Marin D. Detecting the optic disc boundary in digital fundus images using morphological, edge detection, and feature extraction techniques. IEEE Trans Med Imaging 2010;29:1860–1869.
42. Haleem MS, Han L, Hemert JV, Li B, Fleming A, Pasquale LR, et al. A novel adaptive deformable model for automated optic disc and cup segmentation to aid glaucoma diagnosis. J Med Syst 2017;42:20.
43. Hu M, Zhu C, Li X, Xu Y. Optic cup segmentation from fundus images for glaucoma diagnosis. Bioengineered 2017;8:21–28.
44. Wang J, Wang Z, Li F, Qu G, Qiao Y, Lv H, et al. Joint retina segmentation and classification for early glaucoma diagnosis. Biomed Opt Express 2019;10:2639–2656.
45. Zhou W, Yi Y, Gao Y, Dai J. Optic disc and cup segmentation in retinal images for glaucoma diagnosis by locally statistical active contour model with structure prior. Comput Math Methods Med 2019;2019:8973287.
46. Fu H, Cheng J, Xu Y, Zhang C, Wong DWK, Liu J, et al. Discaware ensemble network for glaucoma screening from fundus image. IEEE Trans Med Imaging 2018;37:2493– 2501.
47. Huang D, Swanson EA, Lin CP, Schuman JS, Stinson WG, Chang W, et al. Optical coherence tomography. Science (New York, NY) 1991;254:1178–1181.
48. Hood DC, Fortune B, Arthur SN, Xing D, Salant JA, Ritch R, et al. Blood vessel contributions to retinal nerve fiber layer thickness profiles measured with optical coherence tomography. J Glaucoma 2008;17:519–528.
49. Koozekanani D, Boyer K, Roberts C. Retinal thickness measurements from optical coherence tomography using a Markov boundary model. IEEE Trans Med Imaging 2001;20:900–916.
50. Ishikawa H, Stein DM, Wollstein G, Beaton S, Fujimoto JG, Schuman JS. Macular segmentation with optical coherence tomography. Invest Ophthalmol Vis Sci 2005;46:2012–2017.
51. Kafieh R, Rabbani H, Abramoff MD, Sonka M. Intra-retinal layer segmentation of 3D optical coherence tomography using coarse grained diffusion map. Med Image Anal 2013;17:907–928.
52. Gu Z, Cheng J, Fu H, Zhou K, Hao H, Zhao Y, et al. CE-Net: Context encoder network for 2D medical image segmentation. IEEE T Med Imaging 2019;38:2281–2292.
53. Wilson M, Chopra R, Wilson MZ, Cooper C, MacWilliams P, Liu Y, et al. Validation and clinical applicability of wholevolume automated segmentation of optical coherence tomography in retinal disease using deep learning. JAMA Ophthalmol 2021;139:964–973.
54. Garway-Heath DF, Poinoosawmy D, Fitzke FW, Hitchings RA. Mapping the visual field to the optic disc in normal tension glaucoma eyes. Ophthalmology 2000;107:1809– 1815.
55. Brusini P. Clinical use of a new method for visual field damage classification in glaucoma. Eur J Ophthalmol 1996;6:402–407. 56. Keltner JL, Johnson CA, Cello KE, Edwards MA, Bandermann SE, Kass MA, et al. Classification of visual field abnormalities in the ocular hypertension treatment study. Arch Ophthalmol 2003;121:643–650.
57. Lichter PR. Variability of expert observers in evaluating the optic disc. Trans Am Ophthalmol Soc 1976;74:532–572.
58. Jampel HD, Friedman D, Quigley H, Vitale S, Miller R, Knezevich F, et al. Agreement among glaucoma specialists in assessing progressive disc changes from photographs in open-angle glaucoma patients. Am J Ophthalmol 2009;147:39–44 e31.
59. Damms T, Dannheim F. Sensitivity and specificity of optic disc parameters in chronic glaucoma. Invest Ophthalmol Vis Sci 1993;34:2246–2250.
60. Gundersen KG, Heijl A, Bengtsson B. Sensitivity and specificity of structural optic disc parameters in chronic glaucoma. Acta Ophthalmol Scand 1996;74:120–125.
61. 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;35:3362–3373.
62. Madsen EM, Yolton RL. Demonstration of a neural network expert system for recognition of glaucomatous visual field changes. Mil Med 1994;159:553–557.
63. Spenceley SE, Henson DB, Bull DR. Visual field analysis using artificial neural networks. Ophthalmic Physiol Opt 1994;14:239–248.
64. Lietman T, Eng J, Katz J, Quigley HA. Neural networks for visual field analysis: How do they compare with other algorithms? J Glaucoma 1999;8:77–80.
65. 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;18:35.
66. Huang X, Jin K, Zhu J, Xue Y, Si K, Zhang C, et al. A structure-related fine-grained deep learning system with diversity data for universal glaucoma visual field grading. Front Med 2022;9:832920.
67. Bowd C, Medeiros FA, Zhang ZH, Zangwill LM, Hao JC, Lee TW, et al. Relevance vector machine and support vector machine classifier analysis of scanning laser polarimetry retinal nerve fiber layer measurements. Investig Ophthalmol Vis Sci 2005;46:1322–1329.
68. Burgansky-Eliash Z, Wollstein G, Bilonick RA, Ishikawa H, Kagemann L, Schuman JS. Glaucoma detection with the Heidelberg retina tomograph 3. Ophthalmology 2007;114:466–471.
69. Bock R, Meier J, Nyul LG, Hornegger J, Michelson G. Glaucoma risk index: Automated glaucoma detection from color fundus images. Med Image Anal 2010;14:471–481.
70. Acharya UR, Dua S, Du X, Sree SV, Chua CK. Automated diagnosis of glaucoma using texture and higher order spectra features. IEEE Trans Inf Technol Biomed: A publication of the IEEE Eng Med Biol Soc 2011;15:449–455.
71. Cheng J, Liu J, Xu Y, Yin F, Wong DW, Tan NM, et al. Superpixel classification based optic disc and optic cup segmentation for glaucoma screening. IEEE Trans Med Imaging 2013;32:1019–1032. 72. Chen X, Xu Y, Yin F, Zhang Z, Wong DW, Wong TY, et al. Multiple ocular diseases detection based on joint sparse multi-task learning. Annu Int Conf IEEE Eng Med Biol Soc 2015;2015:5260–5263.
73. Xiangyu C, Yanwu X, Damon Wing Kee W, Tien Yin W, Jiang L. Glaucoma detection based on deep convolutional neural network. Annu Int Conf IEEE Eng Med Biol Soc 2015;2015:715–718.
74. Ting DSW, Cheung CY, Lim G, Tan GSW, Quang ND, Gan A, et al. Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. JAMA 2017;318:2211–2223.
75. 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;125:1199–1206.
76. Liu H, Li L, Wormstone IM, Qiao C, 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;137:1353–1360.
77. Thakur A, Goldbaum M, Yousefi S. Predicting glaucoma before onset using deep learning. Ophthalmol Glaucoma 2020;3:262–268.
78. Li F, Su Y, Lin F, Li Z, Song Y, Nie S, et al. A deep-learning system predicts glaucoma incidence and progression using retinal photographs. J Clin Invest 2022;132:e157968.
79. Liu S, Graham SL, Schulz A, Kalloniatis M, Zangerl B, Cai W, et al. A deep learning-based algorithm identifies glaucomatous discs using monoscopic fundus photographs. Ophthalmol Glaucoma 2018;1:15–22.
80. Aggarwal R, Sounderajah V, Martin G, Ting DSW, Karthikesalingam A, King D, et al. Diagnostic accuracy of deep learning in medical imaging: A systematic review and meta-analysis. NPJ Digit Med 2021;4:65.
81. Bowd C, Zangwill LM, Berry CC, Blumenthal EZ, Vasile C, Sanchez-Galeana C, et al. Detecting early glaucoma by assessment of retinal nerve fiber layer thickness and visual function. Invest Ophthalmol Vis Sci 2001;42:1993–2003.
82. 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– 2299.
83. Xu J, Ishikawa H, Wollstein G, Bilonick RA, Folio LS, Nadler Z, et al. Three-dimensional spectral-domain optical coherence tomography data analysis for glaucoma detection. PLoS One 2013;8:e55476.
84. Mwanza JC, Oakley JD, Budenz DL, Anderson DR, Cirrus Optical Coherence Tomography Normative Database Study G. Ability of cirrus HD-OCT optic nerve head parameters to discriminate normal from glaucomatous eyes. Ophthalmology 2011;118:241–248 e241.
85. 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.
86. Lisboa R, Paranhos A, Jr., Weinreb RN, Zangwill LM, Leite MT, Medeiros FA. Comparison of different spectral domain OCT scanning protocols for diagnosing preperimetric glaucoma. Invest Ophthalmol Vis Sci 2013;54:3417–3425.
87. Hatanaka Y, Muramatsu C, Sawada A, Hara T, Yamamoto T, Fujita H. Glaucoma risk assessment based on clinical data and automated nerve fiber layer defects detection. Conference proceedings: Annu Int Conf IEEE Eng Med Biol Soc IEEE Eng Med Biol Soc Conf 2012;2012:5963–5966.
88. Burgansky-Eliash Z, Wollstein G, Chu T, Ramsey JD, Glymour C, Noecker RJ, et al. Optical coherencdetection: A preliminary study. Invest Ophthalmol Vis Sci 2005;46:4147–4152.
89. 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;88:44–52.
90. Larrosa JM, Polo V, Ferreras A, Garcia-Martin E, Calvo P, Pablo LE. Neural network analysis of different segmentation strategies of nerve fiber layer assessment for glaucoma diagnosis. J Glaucoma 2015;24:672–678.
91. Ran AR, Wang X, Chan PP, Chan NC, Yip W, Young AL, et al. Three-dimensional multi-task deep learning model to detect glaucomatous optic neuropathy and myopic features from optical coherence tomography scans: A retrospective multi-centre study. Front Med 2022;9:860574.
92. WuDunn D, Takusagawa HL, Sit AJ, Rosdahl JA, Radhakrishnan S, Hoguet A, et al. OCT angiography for the diagnosis of glaucoma: A report by the American Academy of Ophthalmology. Ophthalmology 2021;128:1222–1235.
93. Lee EJ, Lee KM, Lee SH, Kim TW. OCT angiography of the peripapillary retina in primary open-angle glaucoma. Invest Ophthalmol Vis Sci 2016;57:6265–6270.
94. Rao HL, Kadambi SV, Weinreb RN, Puttaiah NK, Pradhan ZS, Rao DAS, et al. Diagnostic ability of peripapillary vessel density measurements of optical coherence tomography angiography in primary open-angle and angle-closure glaucoma. Br J Ophthalmol 2017;101:1066–1070.
95. Yarmohammadi A, Zangwill LM, Diniz-Filho A, Suh MH, Manalastas PI, Fatehee N, et al. Optical coherence tomography angiography vessel density in healthy, glaucoma suspect, and glaucoma eyes. Invest Ophthalmol Vis Sci 2016;57:OCT451–OCT459.
96. 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:e0214875.
97. 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;3:14–24.
98. Abramoff MD, Lavin PT, Birch M, Shah N, Folk JC. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. NPJ Digit Med 2018;1:39.
99. Perez-Rueda A, Jimenez-Rodriguez D, Castro-Luna G. Diagnosis of subclinical keratoconus with a combined model of biomechanical and topographic parameters. J Clin Med 2021;10:2746.
100. Henson DB, Chaudry S, Artes PH, Faragher EB, Ansons A. Response variability in the visual field: Comparison of optic neuritis, glaucoma, ocular hypertension, and normal eyes. Invest Ophthalmol Vis Sci 2000;41:417–421.
101. Mikelberg FS, Parfitt CM, Swindale NV, Graham SL, Drance SM, Gosine R. Ability of the Heidelberg retina tomograph to detect early glaucomatous visual field loss. J Glaucoma 1995;4:242–247.
102. Advanced Glaucoma Intervention Study. 2. Visual field test scoring and reliability. Ophthalmology 1994;101:1445– 1455.
103. Katz J. Scoring systems for measuring progression of visual field loss in clinical trials of glaucoma treatment. Ophthalmology 1999;106:391–395.
104. Gardiner SK, Crabb DP. Examination of different pointwise linear regression methods for determining visual field progression. Invest Ophthalmol Vis Sci 2002;43:1400–1407. O’Leary N, Chauhan BC, Artes PH. Visual field progression in glaucoma: Estimating the overall significance of deterioration with permutation analyses of pointwise linear regression (PoPLR). Invest Ophthalmol Vis Sci 2012;53:6776–6784.
106. Gardiner SK, Demirel S. Detecting change using standard global perimetric indices in glaucoma. Am J Ophthalmol 2017;176:148–156.
107. Zhu H, Russell RA, Saunders LJ, Ceccon S, Garway- Heath DF, Crabb DP. Detecting changes in retinal function: Analysis with non-stationary Weibull error regression and spatial enhancement (ANSWERS). PLoS One 2014;9:e85654.
108. Hu R, Marin-Franch I, Racette L. Prediction accuracy of a novel dynamic structure-function model for glaucoma progression. Invest Ophthalmol Vis Sci 2014;55:8086– 8094.
109. Lin A, Hoffman D, Gaasterland DE, Caprioli J. Neural networks to identify glaucomatous visual field progression. Am J Ophthalmol 2003;135:49–54.
110. Sample PA, Boden C, Zhang Z, Pascual J, Lee TW, Zangwill LM, et al. Unsupervised machine learning with independent component analysis to identify areas of progression in glaucomatous visual fields. Invest Ophthalmol Vis Sci 2005;46:3684–3692.
111. Goldbaum MH, Lee I, Jang G, Balasubramanian M, Sample PA, Weinreb RN, et al. Progression of patterns (POP): A machine classifier algorithm to identify glaucoma progression in visual fields. Invest Ophthalmol Vis Sci 2012;53:6557–6567.
112. Yousefi S, Kiwaki T, Zheng Y, Sugiura H, Asaoka R, Murata H, et al. Detection of longitudinal visual field progression in glaucoma using machine learning. Am J Ophthalmol 2018;193:71–79.
113. Wollstein G, Schuman JS, Price LL, Aydin A, Stark PC, Hertzmark E, et al. Optical coherence tomography longitudinal evaluation of retinal nerve fiber layer thickness in glaucoma. Arch Ophthalmol 2005;123:464– 470.
114. Na JH, Sung KR, Lee JR, Lee KS, Baek S, Kim HK, et al. Detection of glaucomatous progression by spectraldomain optical coherence tomography. Ophthalmology 2013;120:1388–1395.
115. Yousefi S, Goldbaum MH, Balasubramanian M, Jung TP, Weinreb RN, Medeiros FA, et al. Glaucoma progression detection using structural retinal nerve fiber layer measurements and functional visual field points. IEEE Trans Biomed Eng 2014;61:1143–1154.
116. Wang M, Shen LQ, Pasquale LR, Petrakos P, Formica S, Boland MV, et al. An artificial intelligence approach to detect visual field progression in glaucoma based on spatial pattern analysis. Invest Ophthalmol Vis Sci 2019;60:365–375.
117. Yousefi S, Pasquale LR, Boland MV, Johnson CA. Machineidentified patterns of visual field loss and an association with rapid progression in the ocular hypertension treatment study. Ophthalmology 2022;129:1402–1411.
118. Dixit A, Yohannan J, Boland MV. Assessing glaucoma progression using machine learning trained on longitudinal visual field and clinical data. Ophthalmology 2020;128:1016–1026.
119. Pathak M, Demirel S, Gardiner SK. Nonlinear trend analysis of longitudinal pointwise visual field sensitivity in suspected and early glaucoma. Transl Vis Sci Technol 2015;4:8.
120. Chen A, Nouri-Mahdavi K, Otarola FJ, Yu F, Afifi AA, Caprioli J. Models of glaucomatous visual field loss. Invest Ophthalmol Vis Sci 2014;55:7881–7887.
121. 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;51:5657–5666.
122. Bogunovic H, Kwon YH, Rashid A, Lee K, Critser DB, Garvin MK, et al. Relationships of retinal structure and humphrey 24-2 visual field thresholds in patients with glaucoma. Invest Ophthalmol Vis Sci 2014;56:259–271.
123. Guo Z, Kwon YH, Lee K, Wang K, Wahle A, Alward WLM, et al. Optical coherence tomography analysis based prediction of Humphrey 24-2 visual field thresholds in patients with glaucoma. Invest Ophthalmol Vis Sci 2017;58:3975–3985.
124. 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. Kdd’18: Proc 24th Acm Sigkdd Int Conf Knowl Discov Data Min 2018:783–792.
125. 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;127:346–356.
126. Yu HH, Maetschke SR, Antony BJ, Ishikawa H, Wollstein G, Schuman JS, et al. Estimating global visual field indices in glaucoma by combining macula and optic disc OCT scans using 3-dimensional convolutional neural networks. Ophthalmol Glaucoma 2021;4:102–112.
127. Huang X, Sun J, Majoor J, Vermeer KA, Lemij H, Elze T, et al. Estimating the severity of visual field damage from retinal nerve fiber layer thickness measurements with artificial intelligence. Transl Vis Sci Technol 2021;10:16.
128. Huang X, Sun J, Gupta K, Montesano G, Crabb DP, Garway-Heath DF, et al. Detecting glaucoma from multimodal data using probabilistic deep learning. Front Med 2022;9:923096.
129. Prum BE, Jr., Rosenberg LF, Gedde SJ, Mansberger SL, Stein JD, Moroi SE, et al. Primary open-angle glaucoma preferred practice pattern((R)) guidelines. Ophthalmology 2016;123:P41–P111.
130. European Glaucoma Society Terminology and Guidelines for Glaucoma, 4th Edition - Chapter 2: Classification and terminology. Supported by the EGS Foundation: Part 1: Foreword; Introduction; Glossary; Chapter 2 Classification and terminology. Br J Ophthalmol 2017;101:73–127.
131. Sounderajah V, Ashrafian H, Golub RM, Shetty S, De Fauw J, Hooft L, et al. Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: The STARD-AI protocol. BMJ Open 2021;11:e047709.
132. Cruz Rivera S, Liu X, Chan AW, Denniston AK, Calvert MJ. Guidelines for clinical trial protocols for interventions involving artificial intelligence: The SPIRIT-AI extension. Nat Med 2020;26:1351–1363.
133. Liu X, Rivera SC, Moher D, Calvert MJ, Denniston AK. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: The CONSORT-AI Extension. BMJ 2020;370:m3164.105.
2. 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;316:2402–2410.
3. De Fauw J, Ledsam JR, Romera-Paredes B, Nikolov S, Tomasev N, Blackwell S, et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat Med 2018;24:1342–1350.
4. Kelman SE, Perell HF, D’Autrechy L, Scott RJ. A neural network can differentiate glaucoma and optic neuropathy visual fields through pattern recognition. In: Mills RP, Heijl A, editors. Perimetry Update 1990/1991, Proceedings of the IXth International Perimetric Society Meeting. Amsterdam/New York: Kugler Publications; 1991. p. 291– 295.
5. Nagata S, Kani K, Sugiyama A. A computer assisted visual field diagnosis system using neural netowrks. In: Mills RP, Heijl A, editors. Perimetry Update 1990/1991, Proceedings of the IXth International Perimetric Society Meeting. Amsterdam/New York: Kugler Publications; 1991. p. 291–295.
6. Mendels F, Heneghan C, Thiran J-P. Identification of the optic disk boundary in retinal images using active contours. 1999.
7. 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;49:963–974.
8. Bengtsson B, Bizios D, Heijl A. Effects of input data on the performance of a neural network in distinguishing normal and glaucomatous visual fields. Invest Ophthalmol Vis Sci 2005;46:3730–3736. 9. Bizios D, Heijl A, Bengtsson B. Trained artificial neural network for glaucoma diagnosis using visual field data: A comparison with conventional algorithms. J Glaucoma 2007;16:20–28.
10. Wroblewski D, Francis B, Chopra V, Kawji AS, Quiros P, Dustin L, et al. Glaucoma detection and evaluation through pattern recognition in standard automated perimetry data. Graefes Arch Clin Exp Ophthalmol 2009;247:1517–1530.
11. Chrastek R, Wolf M, Donath K, Niemann H, Paulus D, Hothorn T, et al. Automated segmentation of the optic nerve head for diagnosis of glaucoma. Med Image Anal 2005;9:297–314.
12. Lim G, Cheng Y, Hsu W, Lee ML. Integrated optic disc and cup segmentation with deep learning. Paper presented at: 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI); 2015 Nov 9-11 Nov.
13. Lee CS, Tyring AJ, Deruyter NP, Wu Y, Rokem A, Lee AY. Deep-learning based, automated segmentation of macular edema in optical coherence tomography. Biomed Opt Express 2017;8:3440–3448.
14. Kugelman J, Alonso-Caneiro D, Read SA, Vincent SJ, Collins MJ. Automatic segmentation of OCT retinal boundaries using recurrent neural networks and graph search. Biomed Opt Express 2018;9:5759–5777.
15. Masood S, Fang R, Li P, Li H, Sheng B, Mathavan A, et al. Automatic choroid layer segmentation from optical coherence tomography images using deep learning. Sci Rep 2019;9:3058.
16. Zhang H, Yang J, Zhou K, Li F, Hu Y, Zhao Y, et al. Automatic segmentation and visualization of choroid in OCT with knowledge infused deep learning. IEEE J Biomed Health Inform 2020;24:3408–3420.
17. Wu Q, Zhang B, Hu Y, Liu B, Cao D, Yang D, et al. Detection of morphologic patterns of diabetic macular edema using a deep learning approach based on optical coherence tomography images. Retina 2020.
18. 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;198:136–145.
19. Lee J, Kim YK, Park KH, Jeoung JW. Diagnosing glaucoma with spectral-domain optical coherence tomography using deep learning classifier. J Glaucoma 2020;29:287–294.
20. Ran AR, Cheung CY, Wang X, Chen H, Luo LY, Chan PP, et al. Detection of glaucomatous optic neuropathy with spectral-domain optical coherence tomography: A retrospective training and validation deep-learning analysis. Lancet Digit Health 2019;1:e172–e182.
21. 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;138:333–339.
22. 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. Investig Ophthalmol Vis Sci 2004;45:2596–2605.
23. Goldbaum MH, Sample PA, Zhang Z, Chan K, Hao J, Lee TW, et al. Using unsupervised learning with independent component analysis to identify patterns of glaucomatous visual field defects. Investig Ophthalmol Vis Sci 2005;46:3676–3683.
24. Bowd C, Weinreb RN, Balasubramanian M, Lee I, Jang G, Yousefi S, et al.Glaucomatous patterns in frequency doubling technology (FDT) perimetry data identified by unsupervised machine learning classifiers. PLoS One 2014;9:e85941
25. Yousefi S, Goldbaum MH, Balasubramanian M, Medeiros FA, Zangwill LM, Liebmann JM, et al. Learning from data: Recognizing glaucomatous defect patterns and detecting progression from visual field measurements. IEEE Trans Biomed Eng 2014;61:2112–2124.
26. Yousefi S, Goldbaum MH, Zangwill LM, Medeiros FA, Bowd C. Recognizing patterns of visual field loss using unsupervised machine learning. Proc SPIE Int Soc Opt Eng 2014;2014:90342M.
27. Elze T, Pasquale LR, Shen LQ, Chen TC, Wiggs JL, Bex PJ. Patterns of functional vision loss in glaucoma determined with archetypal analysis. J R Soc Interface 2015;12:20141118.
28. Yousefi S, Balasubramanian M, Goldbaum MH, Medeiros FA, Zangwill LM, Weinreb RN, et al. Unsupervised Gaussian mixture-model with expectation maximization for detecting glaucomatous progression in standard automated perimetry visual fields. Transl Vis Sci Technol 2016;5:2.
29. Wang M, Shen LQ, Pasquale LR, Boland MV, Wellik SR, De Moraes CG, et al. Artificial intelligence classification of central visual field patterns in glaucoma. Ophthalmology.
30. Thakur A, Goldbaum M, Yousefi S. Convex representations using deep archetypal analysis for predicting glaucoma. IEEE J Transl Eng Health Med 2020;8:3800107.
31. Gupta K, Thakur A, Goldbaum M, Yousefi S. Glaucoma precognition: Recognizing preclinical visual functional signs of glaucoma. Paper presented at: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW); 2020 June 14–19.
32. Nadler Z, Wollstein G, Ishikawa H, Schuman JS. Clinical application of ocular imaging. Optom Vis Sci 2012;89:E543–E553.
33. Stein JD, Talwar N, Laverne AM, Nan B, Lichter PR. Trends in use of ancillary glaucoma tests for patients with open-angle glaucoma from 2001 to 2009. Ophthalmology 2012;119:748–758.
34. Alencar LM, Medeiros FA. The role of standard automated perimetry and newer functional methods for glaucoma diagnosis and follow-up. Indian J Ophthalmol 2011;59:S53–S58.
35. Bengtsson B, Heijl A. A visual field index for calculation of glaucoma rate of progression. Am J Ophthalmol 2008;145:343–353.
36. Phene S, Dunn RC, Hammel N, Liu Y, Krause J, Kitade N, et al. Deep learning and glaucoma specialists: The relative importance of optic disc features to predict glaucoma referral in fundus photographs. Ophthalmology 2019;126:1627–1639.
37. Bussel, II, Wollstein G, Schuman JS. OCT for glaucoma diagnosis, screening and detection of glaucoma progression. Br J Ophthalmol 2014;98:ii15–19.
38. Brusini P, Johnson CA. Staging functional damage in glaucoma: Review of different classification methods. Surv Ophthalmol 2007;52:156–179.
39. Hoover A, Goldbaum M. Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels. IEEE Trans Med Imaging 2003;22:951–958.
40. Wong DW, Liu J, Lim JH, Tan NM, Zhang Z, Lu S, et al. Intelligent fusion of cup-to-disc ratio determination methods for glaucoma detection in ARGALI. Annu Int Conf IEEE Eng Med Biol Soc 2009;2009:5777–5780.
41. Aquino A, Gegundez-Arias ME, Marin D. Detecting the optic disc boundary in digital fundus images using morphological, edge detection, and feature extraction techniques. IEEE Trans Med Imaging 2010;29:1860–1869.
42. Haleem MS, Han L, Hemert JV, Li B, Fleming A, Pasquale LR, et al. A novel adaptive deformable model for automated optic disc and cup segmentation to aid glaucoma diagnosis. J Med Syst 2017;42:20.
43. Hu M, Zhu C, Li X, Xu Y. Optic cup segmentation from fundus images for glaucoma diagnosis. Bioengineered 2017;8:21–28.
44. Wang J, Wang Z, Li F, Qu G, Qiao Y, Lv H, et al. Joint retina segmentation and classification for early glaucoma diagnosis. Biomed Opt Express 2019;10:2639–2656.
45. Zhou W, Yi Y, Gao Y, Dai J. Optic disc and cup segmentation in retinal images for glaucoma diagnosis by locally statistical active contour model with structure prior. Comput Math Methods Med 2019;2019:8973287.
46. Fu H, Cheng J, Xu Y, Zhang C, Wong DWK, Liu J, et al. Discaware ensemble network for glaucoma screening from fundus image. IEEE Trans Med Imaging 2018;37:2493– 2501.
47. Huang D, Swanson EA, Lin CP, Schuman JS, Stinson WG, Chang W, et al. Optical coherence tomography. Science (New York, NY) 1991;254:1178–1181.
48. Hood DC, Fortune B, Arthur SN, Xing D, Salant JA, Ritch R, et al. Blood vessel contributions to retinal nerve fiber layer thickness profiles measured with optical coherence tomography. J Glaucoma 2008;17:519–528.
49. Koozekanani D, Boyer K, Roberts C. Retinal thickness measurements from optical coherence tomography using a Markov boundary model. IEEE Trans Med Imaging 2001;20:900–916.
50. Ishikawa H, Stein DM, Wollstein G, Beaton S, Fujimoto JG, Schuman JS. Macular segmentation with optical coherence tomography. Invest Ophthalmol Vis Sci 2005;46:2012–2017.
51. Kafieh R, Rabbani H, Abramoff MD, Sonka M. Intra-retinal layer segmentation of 3D optical coherence tomography using coarse grained diffusion map. Med Image Anal 2013;17:907–928.
52. Gu Z, Cheng J, Fu H, Zhou K, Hao H, Zhao Y, et al. CE-Net: Context encoder network for 2D medical image segmentation. IEEE T Med Imaging 2019;38:2281–2292.
53. Wilson M, Chopra R, Wilson MZ, Cooper C, MacWilliams P, Liu Y, et al. Validation and clinical applicability of wholevolume automated segmentation of optical coherence tomography in retinal disease using deep learning. JAMA Ophthalmol 2021;139:964–973.
54. Garway-Heath DF, Poinoosawmy D, Fitzke FW, Hitchings RA. Mapping the visual field to the optic disc in normal tension glaucoma eyes. Ophthalmology 2000;107:1809– 1815.
55. Brusini P. Clinical use of a new method for visual field damage classification in glaucoma. Eur J Ophthalmol 1996;6:402–407. 56. Keltner JL, Johnson CA, Cello KE, Edwards MA, Bandermann SE, Kass MA, et al. Classification of visual field abnormalities in the ocular hypertension treatment study. Arch Ophthalmol 2003;121:643–650.
57. Lichter PR. Variability of expert observers in evaluating the optic disc. Trans Am Ophthalmol Soc 1976;74:532–572.
58. Jampel HD, Friedman D, Quigley H, Vitale S, Miller R, Knezevich F, et al. Agreement among glaucoma specialists in assessing progressive disc changes from photographs in open-angle glaucoma patients. Am J Ophthalmol 2009;147:39–44 e31.
59. Damms T, Dannheim F. Sensitivity and specificity of optic disc parameters in chronic glaucoma. Invest Ophthalmol Vis Sci 1993;34:2246–2250.
60. Gundersen KG, Heijl A, Bengtsson B. Sensitivity and specificity of structural optic disc parameters in chronic glaucoma. Acta Ophthalmol Scand 1996;74:120–125.
61. 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;35:3362–3373.
62. Madsen EM, Yolton RL. Demonstration of a neural network expert system for recognition of glaucomatous visual field changes. Mil Med 1994;159:553–557.
63. Spenceley SE, Henson DB, Bull DR. Visual field analysis using artificial neural networks. Ophthalmic Physiol Opt 1994;14:239–248.
64. Lietman T, Eng J, Katz J, Quigley HA. Neural networks for visual field analysis: How do they compare with other algorithms? J Glaucoma 1999;8:77–80.
65. 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;18:35.
66. Huang X, Jin K, Zhu J, Xue Y, Si K, Zhang C, et al. A structure-related fine-grained deep learning system with diversity data for universal glaucoma visual field grading. Front Med 2022;9:832920.
67. Bowd C, Medeiros FA, Zhang ZH, Zangwill LM, Hao JC, Lee TW, et al. Relevance vector machine and support vector machine classifier analysis of scanning laser polarimetry retinal nerve fiber layer measurements. Investig Ophthalmol Vis Sci 2005;46:1322–1329.
68. Burgansky-Eliash Z, Wollstein G, Bilonick RA, Ishikawa H, Kagemann L, Schuman JS. Glaucoma detection with the Heidelberg retina tomograph 3. Ophthalmology 2007;114:466–471.
69. Bock R, Meier J, Nyul LG, Hornegger J, Michelson G. Glaucoma risk index: Automated glaucoma detection from color fundus images. Med Image Anal 2010;14:471–481.
70. Acharya UR, Dua S, Du X, Sree SV, Chua CK. Automated diagnosis of glaucoma using texture and higher order spectra features. IEEE Trans Inf Technol Biomed: A publication of the IEEE Eng Med Biol Soc 2011;15:449–455.
71. Cheng J, Liu J, Xu Y, Yin F, Wong DW, Tan NM, et al. Superpixel classification based optic disc and optic cup segmentation for glaucoma screening. IEEE Trans Med Imaging 2013;32:1019–1032. 72. Chen X, Xu Y, Yin F, Zhang Z, Wong DW, Wong TY, et al. Multiple ocular diseases detection based on joint sparse multi-task learning. Annu Int Conf IEEE Eng Med Biol Soc 2015;2015:5260–5263.
73. Xiangyu C, Yanwu X, Damon Wing Kee W, Tien Yin W, Jiang L. Glaucoma detection based on deep convolutional neural network. Annu Int Conf IEEE Eng Med Biol Soc 2015;2015:715–718.
74. Ting DSW, Cheung CY, Lim G, Tan GSW, Quang ND, Gan A, et al. Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. JAMA 2017;318:2211–2223.
75. 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;125:1199–1206.
76. Liu H, Li L, Wormstone IM, Qiao C, 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;137:1353–1360.
77. Thakur A, Goldbaum M, Yousefi S. Predicting glaucoma before onset using deep learning. Ophthalmol Glaucoma 2020;3:262–268.
78. Li F, Su Y, Lin F, Li Z, Song Y, Nie S, et al. A deep-learning system predicts glaucoma incidence and progression using retinal photographs. J Clin Invest 2022;132:e157968.
79. Liu S, Graham SL, Schulz A, Kalloniatis M, Zangerl B, Cai W, et al. A deep learning-based algorithm identifies glaucomatous discs using monoscopic fundus photographs. Ophthalmol Glaucoma 2018;1:15–22.
80. Aggarwal R, Sounderajah V, Martin G, Ting DSW, Karthikesalingam A, King D, et al. Diagnostic accuracy of deep learning in medical imaging: A systematic review and meta-analysis. NPJ Digit Med 2021;4:65.
81. Bowd C, Zangwill LM, Berry CC, Blumenthal EZ, Vasile C, Sanchez-Galeana C, et al. Detecting early glaucoma by assessment of retinal nerve fiber layer thickness and visual function. Invest Ophthalmol Vis Sci 2001;42:1993–2003.
82. 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– 2299.
83. Xu J, Ishikawa H, Wollstein G, Bilonick RA, Folio LS, Nadler Z, et al. Three-dimensional spectral-domain optical coherence tomography data analysis for glaucoma detection. PLoS One 2013;8:e55476.
84. Mwanza JC, Oakley JD, Budenz DL, Anderson DR, Cirrus Optical Coherence Tomography Normative Database Study G. Ability of cirrus HD-OCT optic nerve head parameters to discriminate normal from glaucomatous eyes. Ophthalmology 2011;118:241–248 e241.
85. 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.
86. Lisboa R, Paranhos A, Jr., Weinreb RN, Zangwill LM, Leite MT, Medeiros FA. Comparison of different spectral domain OCT scanning protocols for diagnosing preperimetric glaucoma. Invest Ophthalmol Vis Sci 2013;54:3417–3425.
87. Hatanaka Y, Muramatsu C, Sawada A, Hara T, Yamamoto T, Fujita H. Glaucoma risk assessment based on clinical data and automated nerve fiber layer defects detection. Conference proceedings: Annu Int Conf IEEE Eng Med Biol Soc IEEE Eng Med Biol Soc Conf 2012;2012:5963–5966.
88. Burgansky-Eliash Z, Wollstein G, Chu T, Ramsey JD, Glymour C, Noecker RJ, et al. Optical coherencdetection: A preliminary study. Invest Ophthalmol Vis Sci 2005;46:4147–4152.
89. 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;88:44–52.
90. Larrosa JM, Polo V, Ferreras A, Garcia-Martin E, Calvo P, Pablo LE. Neural network analysis of different segmentation strategies of nerve fiber layer assessment for glaucoma diagnosis. J Glaucoma 2015;24:672–678.
91. Ran AR, Wang X, Chan PP, Chan NC, Yip W, Young AL, et al. Three-dimensional multi-task deep learning model to detect glaucomatous optic neuropathy and myopic features from optical coherence tomography scans: A retrospective multi-centre study. Front Med 2022;9:860574.
92. WuDunn D, Takusagawa HL, Sit AJ, Rosdahl JA, Radhakrishnan S, Hoguet A, et al. OCT angiography for the diagnosis of glaucoma: A report by the American Academy of Ophthalmology. Ophthalmology 2021;128:1222–1235.
93. Lee EJ, Lee KM, Lee SH, Kim TW. OCT angiography of the peripapillary retina in primary open-angle glaucoma. Invest Ophthalmol Vis Sci 2016;57:6265–6270.
94. Rao HL, Kadambi SV, Weinreb RN, Puttaiah NK, Pradhan ZS, Rao DAS, et al. Diagnostic ability of peripapillary vessel density measurements of optical coherence tomography angiography in primary open-angle and angle-closure glaucoma. Br J Ophthalmol 2017;101:1066–1070.
95. Yarmohammadi A, Zangwill LM, Diniz-Filho A, Suh MH, Manalastas PI, Fatehee N, et al. Optical coherence tomography angiography vessel density in healthy, glaucoma suspect, and glaucoma eyes. Invest Ophthalmol Vis Sci 2016;57:OCT451–OCT459.
96. 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:e0214875.
97. 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;3:14–24.
98. Abramoff MD, Lavin PT, Birch M, Shah N, Folk JC. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. NPJ Digit Med 2018;1:39.
99. Perez-Rueda A, Jimenez-Rodriguez D, Castro-Luna G. Diagnosis of subclinical keratoconus with a combined model of biomechanical and topographic parameters. J Clin Med 2021;10:2746.
100. Henson DB, Chaudry S, Artes PH, Faragher EB, Ansons A. Response variability in the visual field: Comparison of optic neuritis, glaucoma, ocular hypertension, and normal eyes. Invest Ophthalmol Vis Sci 2000;41:417–421.
101. Mikelberg FS, Parfitt CM, Swindale NV, Graham SL, Drance SM, Gosine R. Ability of the Heidelberg retina tomograph to detect early glaucomatous visual field loss. J Glaucoma 1995;4:242–247.
102. Advanced Glaucoma Intervention Study. 2. Visual field test scoring and reliability. Ophthalmology 1994;101:1445– 1455.
103. Katz J. Scoring systems for measuring progression of visual field loss in clinical trials of glaucoma treatment. Ophthalmology 1999;106:391–395.
104. Gardiner SK, Crabb DP. Examination of different pointwise linear regression methods for determining visual field progression. Invest Ophthalmol Vis Sci 2002;43:1400–1407. O’Leary N, Chauhan BC, Artes PH. Visual field progression in glaucoma: Estimating the overall significance of deterioration with permutation analyses of pointwise linear regression (PoPLR). Invest Ophthalmol Vis Sci 2012;53:6776–6784.
106. Gardiner SK, Demirel S. Detecting change using standard global perimetric indices in glaucoma. Am J Ophthalmol 2017;176:148–156.
107. Zhu H, Russell RA, Saunders LJ, Ceccon S, Garway- Heath DF, Crabb DP. Detecting changes in retinal function: Analysis with non-stationary Weibull error regression and spatial enhancement (ANSWERS). PLoS One 2014;9:e85654.
108. Hu R, Marin-Franch I, Racette L. Prediction accuracy of a novel dynamic structure-function model for glaucoma progression. Invest Ophthalmol Vis Sci 2014;55:8086– 8094.
109. Lin A, Hoffman D, Gaasterland DE, Caprioli J. Neural networks to identify glaucomatous visual field progression. Am J Ophthalmol 2003;135:49–54.
110. Sample PA, Boden C, Zhang Z, Pascual J, Lee TW, Zangwill LM, et al. Unsupervised machine learning with independent component analysis to identify areas of progression in glaucomatous visual fields. Invest Ophthalmol Vis Sci 2005;46:3684–3692.
111. Goldbaum MH, Lee I, Jang G, Balasubramanian M, Sample PA, Weinreb RN, et al. Progression of patterns (POP): A machine classifier algorithm to identify glaucoma progression in visual fields. Invest Ophthalmol Vis Sci 2012;53:6557–6567.
112. Yousefi S, Kiwaki T, Zheng Y, Sugiura H, Asaoka R, Murata H, et al. Detection of longitudinal visual field progression in glaucoma using machine learning. Am J Ophthalmol 2018;193:71–79.
113. Wollstein G, Schuman JS, Price LL, Aydin A, Stark PC, Hertzmark E, et al. Optical coherence tomography longitudinal evaluation of retinal nerve fiber layer thickness in glaucoma. Arch Ophthalmol 2005;123:464– 470.
114. Na JH, Sung KR, Lee JR, Lee KS, Baek S, Kim HK, et al. Detection of glaucomatous progression by spectraldomain optical coherence tomography. Ophthalmology 2013;120:1388–1395.
115. Yousefi S, Goldbaum MH, Balasubramanian M, Jung TP, Weinreb RN, Medeiros FA, et al. Glaucoma progression detection using structural retinal nerve fiber layer measurements and functional visual field points. IEEE Trans Biomed Eng 2014;61:1143–1154.
116. Wang M, Shen LQ, Pasquale LR, Petrakos P, Formica S, Boland MV, et al. An artificial intelligence approach to detect visual field progression in glaucoma based on spatial pattern analysis. Invest Ophthalmol Vis Sci 2019;60:365–375.
117. Yousefi S, Pasquale LR, Boland MV, Johnson CA. Machineidentified patterns of visual field loss and an association with rapid progression in the ocular hypertension treatment study. Ophthalmology 2022;129:1402–1411.
118. Dixit A, Yohannan J, Boland MV. Assessing glaucoma progression using machine learning trained on longitudinal visual field and clinical data. Ophthalmology 2020;128:1016–1026.
119. Pathak M, Demirel S, Gardiner SK. Nonlinear trend analysis of longitudinal pointwise visual field sensitivity in suspected and early glaucoma. Transl Vis Sci Technol 2015;4:8.
120. Chen A, Nouri-Mahdavi K, Otarola FJ, Yu F, Afifi AA, Caprioli J. Models of glaucomatous visual field loss. Invest Ophthalmol Vis Sci 2014;55:7881–7887.
121. 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;51:5657–5666.
122. Bogunovic H, Kwon YH, Rashid A, Lee K, Critser DB, Garvin MK, et al. Relationships of retinal structure and humphrey 24-2 visual field thresholds in patients with glaucoma. Invest Ophthalmol Vis Sci 2014;56:259–271.
123. Guo Z, Kwon YH, Lee K, Wang K, Wahle A, Alward WLM, et al. Optical coherence tomography analysis based prediction of Humphrey 24-2 visual field thresholds in patients with glaucoma. Invest Ophthalmol Vis Sci 2017;58:3975–3985.
124. 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. Kdd’18: Proc 24th Acm Sigkdd Int Conf Knowl Discov Data Min 2018:783–792.
125. 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;127:346–356.
126. Yu HH, Maetschke SR, Antony BJ, Ishikawa H, Wollstein G, Schuman JS, et al. Estimating global visual field indices in glaucoma by combining macula and optic disc OCT scans using 3-dimensional convolutional neural networks. Ophthalmol Glaucoma 2021;4:102–112.
127. Huang X, Sun J, Majoor J, Vermeer KA, Lemij H, Elze T, et al. Estimating the severity of visual field damage from retinal nerve fiber layer thickness measurements with artificial intelligence. Transl Vis Sci Technol 2021;10:16.
128. Huang X, Sun J, Gupta K, Montesano G, Crabb DP, Garway-Heath DF, et al. Detecting glaucoma from multimodal data using probabilistic deep learning. Front Med 2022;9:923096.
129. Prum BE, Jr., Rosenberg LF, Gedde SJ, Mansberger SL, Stein JD, Moroi SE, et al. Primary open-angle glaucoma preferred practice pattern((R)) guidelines. Ophthalmology 2016;123:P41–P111.
130. European Glaucoma Society Terminology and Guidelines for Glaucoma, 4th Edition - Chapter 2: Classification and terminology. Supported by the EGS Foundation: Part 1: Foreword; Introduction; Glossary; Chapter 2 Classification and terminology. Br J Ophthalmol 2017;101:73–127.
131. Sounderajah V, Ashrafian H, Golub RM, Shetty S, De Fauw J, Hooft L, et al. Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: The STARD-AI protocol. BMJ Open 2021;11:e047709.
132. Cruz Rivera S, Liu X, Chan AW, Denniston AK, Calvert MJ. Guidelines for clinical trial protocols for interventions involving artificial intelligence: The SPIRIT-AI extension. Nat Med 2020;26:1351–1363.
133. Liu X, Rivera SC, Moher D, Calvert MJ, Denniston AK. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: The CONSORT-AI Extension. BMJ 2020;370:m3164.105.