Prediction of visual field progression in glaucoma: existing methods and artificial intelligence.
AI
Glaucoma
Progression
Visual field
Journal
Japanese journal of ophthalmology
ISSN: 1613-2246
Titre abrégé: Jpn J Ophthalmol
Pays: Japan
ID NLM: 0044652
Informations de publication
Date de publication:
Sep 2023
Sep 2023
Historique:
received:
26
12
2022
accepted:
13
04
2023
medline:
1
9
2023
pubmed:
4
8
2023
entrez:
4
8
2023
Statut:
ppublish
Résumé
Timely treatment is essential in the management of glaucoma. However, subjective assessment of visual field (VF) progression is not recommended, because it can be unreliable. There are two types of artificial intelligence (AI) strong and weak (machine learning). Weak AIs can perform specific tasks. Linear regression is a method of weak AI. Using linear regression in the real-world clinic has enabled analyzing and predicting VF progression. However, caution is still required when interpreting the results, because whenever the number of VF data sets investigated is small, the predictions can be inaccurate. Several other non-ordinal, or modern AI methods have been constructed to improve prediction accuracy, such as clustering and more modern AI methods of Analysis with Non-Stationary Weibull Error Regression and Spatial Enhancement (ANSWERS), Variational Bayes Linear Regression (VBLR), Kalman Filter and sparse modeling (The least absolute shrinkage and selection operator regression: Lasso). It is also possible to improve the prediction performance using retinal thickness measured with optical coherence tomography by using machine learning methods, such as multitask learning.
Identifiants
pubmed: 37540325
doi: 10.1007/s10384-023-01009-3
pii: 10.1007/s10384-023-01009-3
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
546-559Subventions
Organisme : ministry of education, culture, sports, science, and technology of Japan
ID : 19H01114
Organisme : ministry of education, culture, sports, science and technology of Japan
ID : 18KK0253
Organisme : ministry of education, culture, sports, science and technology of Japan
ID : 20K09784
Organisme : ministry of education, culture, sports, science and technology of Japan
ID : 80635748
Organisme : japan agency for medical reserach and development
ID : TR-SPRINT
Organisme : the Japan Glaucoma Society Project Support Program
ID : Grant
Informations de copyright
© 2023. Japanese Ophthalmological Society.
Références
Scerri M, Grech V. Artificial intelligence in medicine. Early Hum Dev. 2020;145:105017.
pubmed: 32201033
doi: 10.1016/j.earlhumdev.2020.105017
Jampel HD. Target pressure in glaucoma therapy. J Glaucoma. 1997;6:133–8.
pubmed: 9098822
doi: 10.1097/00061198-199704000-00010
Clement CI, Bhartiya S, Shaarawy T. New perspectives on target intraocular pressure. Surv Ophthalmol. 2014;59:615–26.
pubmed: 25081325
doi: 10.1016/j.survophthal.2014.04.001
The Japan Glaucoma Society. The Japan Glaucoma Society Guidelines for Glaucoma (5th edition). Nippon Ganka Gakkai Zasshi. 2022;126:85–177.
Kiuchi Y, Inoue T, Shoji N, Nakamura M, Tanito M. Glaucoma Guideline Preparation Committee, Japan Glaucoma Society. The Japan Glaucoma Society Guidelines for glaucoma. Jpn J Ophthalmol. 2023;67:189–254.
pubmed: 36780040
doi: 10.1007/s10384-022-00970-9
Viswanathan AC, Crabb DP, McNaught AI, Westcott MC, Kamal D, Garway-Heath DF, et al. Interobserver agreement on visual field progression in glaucoma: a comparison of methods. Br J Ophthalmol. 2003;87:726–30.
pubmed: 12770970
pmcid: 1771729
doi: 10.1136/bjo.87.6.726
Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics. 1977;33:159–74.
pubmed: 843571
doi: 10.2307/2529310
Tanna AP, Budenz DL, Bandi J, Feuer WJ, Feldman RM, Herndon LW, et al. Glaucoma progression analysis software compared with expert consensus opinion in the detection of visual field progression in glaucoma. Ophthalmology. 2012;119:468–73.
pubmed: 22137043
doi: 10.1016/j.ophtha.2011.08.041
Anton A, Pazos M, Martin B, Navero JM, Ayala ME, Castany M, et al. Glaucoma progression detection: agreement, sensitivity, and specificity of expert visual field evaluation, event analysis, and trend analysis. Eur J Ophthalmol. 2013;23:187–95.
pubmed: 23065852
doi: 10.5301/ejo.5000193
Diaz-Aleman VT, Anton A, de la Rosa MG, Johnson ZK, McLeod S, Azuara-Blanco A. Detection of visual-field deterioration by Glaucoma progression analysis and threshold Noiseless Trend programs. Br J Ophthalmol. 2009;93:322–8.
pubmed: 18621790
doi: 10.1136/bjo.2007.136739
Birch MK, Wishart PK, O’Donnell NP. Determining progressive visual field loss in serial Humphrey visual fields. Ophthalmology. 1995;102:1227–34. discussion 34 – 5.
pubmed: 9097752
doi: 10.1016/S0161-6420(95)30885-8
Roberti G, Michelessi M, Tanga L, Belfonte L, Del Grande LM, Bruno M et al. Glaucoma progression diagnosis: the Agreement between Clinical Judgment and Statistical Software. J Clin Med 2022;11(19):5508
pubmed: 36233376
pmcid: 9573472
doi: 10.3390/jcm11195508
Fitzke FW, Hitchings RA, Poinoosawmy D, McNaught AI, Crabb DP. Analysis of visual field progression in glaucoma. Br J Ophthalmol. 1996;80:40–8.
pubmed: 8664231
pmcid: 505382
doi: 10.1136/bjo.80.1.40
Viswanathan AC, Fitzke FW, Hitchings RA. Early detection of visual field progression in glaucoma: a comparison of PROGRESSOR and STATPAC 2. Br J Ophthalmol. 1997;81:1037–42.
pubmed: 9497460
pmcid: 1722087
doi: 10.1136/bjo.81.12.1037
Smith SD, Katz J, Quigley HA. Analysis of progressive change in automated visual fields in glaucoma. Invest Ophthalmol Vis Sci. 1996;37:1419–28.
pubmed: 8641844
Nouri-Mahdavi K, Brigatti L, Weitzman M, Caprioli J. Comparison of methods to detect visual field progression in glaucoma. Ophthalmology. 1997;104:1228–36.
pubmed: 9261308
doi: 10.1016/S0161-6420(97)30153-5
Mayama C, Araie M, Suzuki Y, Ishida K, Yamamoto T, Kitazawa Y, et al. Statistical evaluation of the diagnostic accuracy of methods used to determine the progression of visual field defects in glaucoma. Ophthalmology. 2004;111:2117–25.
pubmed: 15522380
doi: 10.1016/j.ophtha.2004.06.025
Katz J, Sommer A, Gaasterland DE, Anderson DR. Comparison of analytic algorithms for detecting glaucomatous visual field loss. Arch Ophthalmol. 1991;109:1684–9.
pubmed: 1841576
doi: 10.1001/archopht.1991.01080120068028
Mandava S, Zulauf M, Zeyen T, Caprioli J. An evaluation of clusters in the glaucomatous visual field. Am J Ophthalmol. 1993;116:684–91.
pubmed: 8250069
doi: 10.1016/S0002-9394(14)73466-X
Gardiner SK, Crabb DP. Examination of different pointwise linear regression methods for determining visual field progression. Invest Ophthalmol Vis Sci. 2002;43:1400–7.
pubmed: 11980853
Gardiner SK, Crabb DP, Fitzke FW, Hitchings RA. Reducing noise in suspected glaucomatous visual fields by using a new spatial filter. Vis Res. 2004;44:839–48.
pubmed: 14967209
doi: 10.1016/S0042-6989(03)00474-7
Strouthidis NG, Scott A, Viswanathan AC, Crabb DP, Garway-Heath DF. Monitoring glaucomatous visual field progression: the effect of a novel spatial filter. Invest Ophthalmol Vis Sci. 2007;48:251–7.
pubmed: 17197540
doi: 10.1167/iovs.06-0576
Chen A, Nouri-Mahdavi K, Otarola FJ, Yu F, Afifi AA, Caprioli J. Models of glaucomatous visual field loss. Invest Ophthalmol Vis Sci. 2014;55:7881–7.
pubmed: 25377224
doi: 10.1167/iovs.14-15435
Araie M. Basic and clinical studies of pressure-independent damaging factors of open angle glaucoma. Nippon Ganka Gakkai zasshi. 2011;115:213–36.
pubmed: 21476309
Caprioli J, de Leon JM, Azarbod P, Chen A, Morales E, Nouri-Mahdavi K, et al. Trabeculectomy can improve long-term visual function in Glaucoma. Ophthalmology. 2016;123:117–28.
pubmed: 26602970
doi: 10.1016/j.ophtha.2015.09.027
Taketani Y, Murata H, Fujino Y, Mayama C, Asaoka R. How many visual Fields are required to precisely predict future test results in Glaucoma patients when using different Trend analyses? Invest Ophthalmol Vis Sci. 2015;56:4076–82.
pubmed: 26114484
doi: 10.1167/iovs.14-16341
Bryan SR, Vermeer KA, Eilers PH, Lemij HG, Lesaffre EM. Robust and censored modeling and prediction of progression in glaucomatous visual fields. Invest Ophthalmol Vis Sci. 2013;54:6694–700.
pubmed: 24030462
doi: 10.1167/iovs.12-11185
Omoto T, Asaoka R, Akagi T, Oishi A, Miyata M, Murata H, et al. The number of examinations required for the accurate prediction of the progression of the central 10-degree visual field test in glaucoma. Sci Rep. 2022;12:18843.
pubmed: 36344722
pmcid: 9640563
doi: 10.1038/s41598-022-23604-z
Flammer J, Drance SM, Fankhauser F, Augustiny L. Differential light threshold in automated static perimetry. Factors influencing short-term fluctuation. Arch Ophthalmol. 1984;102:876–9.
pubmed: 6732568
doi: 10.1001/archopht.1984.01040030696021
Flammer J, Drance SM, Zulauf M. Differential light threshold. Short- and long-term fluctuation in patients with glaucoma, normal controls, and patients with suspected glaucoma. Arch Ophthalmol. 1984;102:704–6.
pubmed: 6721758
doi: 10.1001/archopht.1984.01040030560017
Bengtsson B, Heijl A. False-negative responses in glaucoma perimetry: indicators of patient performance or test reliability? Invest Ophthalmol Vis Sci. 2000;41:2201–4.
pubmed: 10892863
Henson DB, Evans J, Chauhan BC, Lane C. Influence of fixation accuracy on threshold variability in patients with open angle glaucoma. Invest Ophthalmol Vis Sci. 1996;37:444–50.
pubmed: 8603850
Artes PH. Progression: things we need to remember but often forget to think about. Optom Vis Sci. 2008;85:380–5.
pubmed: 18521009
doi: 10.1097/OPX.0b013e31817882ee
Krakau CE. A statistical trap in the evaluation of visual field decay. Acta Ophthalmol Suppl. 1985;173:19–21.
pubmed: 3002093
Holmin C, Krakau CE. Regression analysis of the central visual field in chronic glaucoma cases. A follow-up study using automatic perimetry. Acta Ophthalmol (Copenh). 1982;60:267–74.
pubmed: 7136538
doi: 10.1111/j.1755-3768.1982.tb08381.x
Spry PG, Bates AB, Johnson CA, Chauhan BC. Simulation of longitudinal threshold visual field data. Invest Ophthalmol Vis Sci. 2000;41:2192–200.
pubmed: 10892862
Asman P, Heijl A. Arcuate cluster analysis in glaucoma perimetry. J Glaucoma. 1993;2:13–20.
pubmed: 19920477
doi: 10.1097/00061198-199300210-00006
Chauhan BC, Drance SM, Lai C. A cluster analysis for threshold perimetry. Graefes Arch Clin Exp Ophthalmol. 1989;227:216–20.
pubmed: 2737482
doi: 10.1007/BF02172752
Suzuki Y, Araie M, Ohashi Y. Sectorization of the central 30 degrees visual field in glaucoma. Ophthalmology. 1993;100:69–75.
pubmed: 8433830
doi: 10.1016/S0161-6420(93)31691-X
Nouri-Mahdavi K, Mock D, Hosseini H, Bitrian E, Yu F, Afifi A, et al. Pointwise rates of visual field progression cluster according to retinal nerve fiber layer bundles. Invest Ophthalmol Vis Sci. 2012;53:2390–4.
pubmed: 22427560
doi: 10.1167/iovs.11-9021
Hirasawa K, Murata H, Hirasawa H, Mayama C, Asaoka R. Clustering visual field test points based on rates of progression to improve the prediction of future damage. Invest Ophthalmol Vis Sci. 2014;55:7681–5.
pubmed: 25342611
doi: 10.1167/iovs.14-15040
Hirasawa K, Murata H, Asaoka R. Revalidating the usefulness of a “Sector-Wise Regression” Approach to predict glaucomatous visual function progression. Invest Ophthalmol Vis Sci. 2015;56:4332–5.
pubmed: 26176870
doi: 10.1167/iovs.15-16694
Van der Laan MJ, Pollard KS. A new algorithm for hybrid hierarchical clustering with visualization and the bootstrap. J Stat Plan Inference. 2003;117:275–303.
doi: 10.1016/S0378-3758(02)00388-9
Rousseeuw PJ, Silhouettes. A graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math. 1987;20:53–65.
doi: 10.1016/0377-0427(87)90125-7
Pollard KS, Van der Laan MJ. A method to identify significant clusters in gene expression data. Proc SCI 2002. 2002;2:318–25.
Garway-Heath DF, Poinoosawmy D, Fitzke FW, Hitchings RA. Mapping the visual field to the optic disc in normal tension glaucoma eyes. Ophthalmology. 2000;107:1809–15.
pubmed: 11013178
doi: 10.1016/S0161-6420(00)00284-0
Weber J, Dannheim F, Dannheim D. The topographical relationship between optic disc and visual field in glaucoma. Acta Ophthalmol (Copenh). 1990;68:568–74.
pubmed: 2275353
doi: 10.1111/j.1755-3768.1990.tb04789.x
Asman P, Heijl A. Glaucoma hemifield test. Automated visual field evaluation. Arch Ophthalmol. 1992;110:812–9.
pubmed: 1596230
doi: 10.1001/archopht.1992.01080180084033
Rokach L, Maimon O. Chapter 15: clustering methods. In: Maimon O, Rokach L, editors. Data Mining and Knowledge Discovery Handbook. Boston, MA: Springer; 2005. pp. 325–52.
Omoto T, Murata H, Fujino Y, Matsuura M, Yamashita T, Miki A, et al. Validating the usefulness of sectorwise regression of visual field in the central 10 degrees. Br J Ophthalmol. 2022;106:497–501.
pubmed: 33441320
doi: 10.1136/bjophthalmol-2020-317391
White HA. A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica. 1980;48:817–38.
doi: 10.2307/1912934
Crabb DP, Russell RA, Malik R, Anand N, Baker H, Boodhna T, et al. editors. Frequency of visual field testing when monitoring patients newly diagnosed with glaucoma: mixed methods and modelling. Southampton (UK): NIHR Journals Library; 2014.
Zeyen TG, Zulauf M, Caprioli J. Priority of test locations for automated perimetry in glaucoma. Ophthalmology. 1993;100:518–22.
pubmed: 8479710
doi: 10.1016/S0161-6420(93)31612-X
Suzuki Y, Kitazawa Y, Araie M, Yamagami J, Yamamoto T, Ishida K, et al. Mathematical and optimal clustering of test points of the central 30-degree visual field of glaucoma. J Glaucoma. 2001;10:121–8.
pubmed: 11316094
doi: 10.1097/00061198-200104000-00009
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.
pubmed: 24465636
pmcid: 3894992
doi: 10.1371/journal.pone.0085654
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;53:6776–84.
pubmed: 22952123
doi: 10.1167/iovs.12-10049
Garway-Heath DF, Lascaratos G, Bunce C, Crabb DP, Russell RA, Shah A, et al. The United Kingdom Glaucoma treatment study: a multicenter, randomized, placebo-controlled clinical trial: design and methodology. Ophthalmology. 2013;120:68–76.
pubmed: 22986112
doi: 10.1016/j.ophtha.2012.07.028
Garway-Heath DF, Crabb DP, Bunce C, Lascaratos G, Amalfitano F, Anand N, et al. Latanoprost for open-angle glaucoma (UKGTS): a randomised, multicentre, placebo-controlled trial. Lancet. 2015;385:1295–304.
pubmed: 25533656
doi: 10.1016/S0140-6736(14)62111-5
Zhu H, Crabb DP, Ho T, Garway-Heath DF. More Accurate modeling of visual field progression in Glaucoma: ANSWERS. Invest Ophthalmol Vis Sci. 2015;56:6077–83.
pubmed: 26393667
doi: 10.1167/iovs.15-16957
Murata H, Araie M, Asaoka R. A new approach to measure visual field progression in glaucoma patients using variational bayes linear regression. Invest Ophthalmol Vis Sci. 2014;55:8386–92.
pubmed: 25414192
doi: 10.1167/iovs.14-14625
Murata H, Zangwill LM, Fujino Y, Matsuura M, Miki A, Hirasawa K, et al. Validating Variational Bayes Linear Regression Method with Multi-Central Datasets. Invest Ophthalmol Vis Sci. 2018;59:1897–904.
pubmed: 29677350
pmcid: 5886131
doi: 10.1167/iovs.17-22907
Bengtsson B, Olsson J, Heijl A, Rootzen H. A new generation of algorithms for computerized threshold perimetry, SITA. Acta Ophthalmol Scand. 1997;75:368–75.
pubmed: 9374242
doi: 10.1111/j.1600-0420.1997.tb00392.x
Murata H, Asaoka R, Fujino Y, Matsuura M, Hirasawa K, Shimada S, et al. Comparing the usefulness of a new algorithm to measure visual field using the variational Bayes linear regression in glaucoma patients, in comparison to the swedish interactive thresholding algorithm. Br J Ophthalmol. 2022;106:660–6.
pubmed: 33441321
doi: 10.1136/bjophthalmol-2020-318304
Hirasawa K, Murata H, Shimada S, Matsuno M, Shoji N, Asaoka R. Faster algorithms to measure visual field using the variational Bayes linear regression model in glaucoma: comparison with SITA-Fast. Br J Ophthalmol. 2022. https://doi.org/10.1136/bjophthalmol-2021-320523 .
doi: 10.1136/bjophthalmol-2021-320523
pubmed: 36261260
Lefferts EJ, Markley FL, Shuster MD. Kalman Filtering for Spacecraft attitude estimation. J Guid Control Dyn. 1982;5:417–29.
doi: 10.2514/3.56190
Kalman RE. A New Approach to Linear filtering and prediction problems. J Basic Eng. 1960;82:35–45.
doi: 10.1115/1.3662552
Catlin DE. The Discrete Kalman Filter. Estimation, control, and the Discrete Kalman Filter. 71st ed. Springer Science & Business Media; 2012:133–63.
Ederer F, Gaasterland DE, Sullivan EK, Investigators A. The advanced Glaucoma intervention study (AGIS): 1. Study design and methods and baseline characteristics of study patients. Control Clin Trials. 1994;15:299–325.
pubmed: 7956270
doi: 10.1016/0197-2456(94)90046-9
Musch DC, Lichter PR, Guire KE, Standardi CL. The collaborative initial Glaucoma treatment study: study design, methods, and baseline characteristics of enrolled patients. Ophthalmology. 1999;106:653–62.
pubmed: 10201583
doi: 10.1016/S0161-6420(99)90147-1
Musch DC, Gillespie BW, Lichter PR, Niziol LM, Janz NK, Investigators CS. Visual field progression in the collaborative initial Glaucoma treatment study the impact of treatment and other baseline factors. Ophthalmology. 2009;116:200–7.
pubmed: 19019444
doi: 10.1016/j.ophtha.2008.08.051
Garcia GP, Nitta K, Lavieri MS, Andrews C, Liu X, Lobaza E, et al. Using Kalman Filtering to Forecast Disease Trajectory for patients with normal tension Glaucoma. Am J Ophthalmol. 2019;199:111–9.
pubmed: 30336130
doi: 10.1016/j.ajo.2018.10.012
Kingma DP, Welling M. Auto-Encoding Variational Bayes. Preprint. Posted online Dec 20, 2013. arXiv:13126114 [cs, stat]. doi: https://doi.org/10.48550/arXiv.1312.6114 .
Rezende DJ, Mohamed S, Wierstra D. Stochastic Backpropagation and Approximate Inference in Deep Generative Models. Preprint. Posted online Jan 16, 2014. arXiv:14014082 [cs, stat]. https://doi.org/10.48550/arXiv.1401.4082 .
Aggarwal CC. Neural networks and deep learning: a Textbook. Springer; 2018. p. 207–13.
doi: 10.1007/978-3-319-94463-0
Shaojie C, Meng Z, Zhao Q, Electrocardiogram recognization based on variational AutoEncoder. In: Machine learning and biometrics. IntechOpen; 2018
Asaoka R, Murata H, Matsuura M, Fujino Y, Yanagisawa M, Yamashita T. Improving structure-function relationship in glaucomatous visual fields by using a deep learning-based noise reduction approach. Ophthalmol Glaucoma. 2020;3:210–7.
pubmed: 32672618
doi: 10.1016/j.ogla.2020.01.001
Asaoka R, Murata H, Asano S, Matsuura M, Fujino Y, Miki A, et al. The usefulness of the deep learning method of variational autoencoder to reduce measurement noise in glaucomatous visual fields. Sci Rep. 2020;10:7893.
pubmed: 32398783
pmcid: 7217822
doi: 10.1038/s41598-020-64869-6
Berchuck SI, Mukherjee S, Medeiros FA. Estimating Rates of Progression and Predicting Future Visual Fields in Glaucoma using a deep Variational Autoencoder. Sci Rep. 2019;9:18113.
pubmed: 31792321
pmcid: 6888896
doi: 10.1038/s41598-019-54653-6
Tibshirani R. Regression shrinkage and selection via the lasso. J R Stat Soc Series B Methodological. 1996;58:267–88.
Friedman J, Hastie T, Tibshirani R. Regularization Paths for generalized Linear Models via Coordinate Descent. J Stat Softw. 2010;33:1–22.
pubmed: 20808728
pmcid: 2929880
doi: 10.18637/jss.v033.i01
Fujino Y, Murata H, Mayama C, Asaoka R. Applying “Lasso” regression to predict future visual field progression in Glaucoma patients. Invest Ophthalmol Vis Sci. 2015;56:2334–9.
pubmed: 25698708
doi: 10.1167/iovs.15-16445
Asaoka R. Measuring visual field progression in the central 10 degrees using additional information from central 24 degrees visual fields and ‘lasso regression’. PLoS ONE. 2013;8:e72199.
pubmed: 23951295
pmcid: 3741185
doi: 10.1371/journal.pone.0072199
Weinreb RN, Khaw PT. Primary open-angle glaucoma. Lancet. 2004;363:1711–20.
pubmed: 15158634
doi: 10.1016/S0140-6736(04)16257-0
Fechtner RD, Weinreb RN. Mechanisms of optic nerve damage in primary open angle glaucoma. Surv Ophthalmol. 1994;39:23–42.
pubmed: 7974188
doi: 10.1016/S0039-6257(05)80042-6
Chauhan BC, Nicolela MT, Artes PH. Incidence and rates of visual field progression after longitudinally measured optic disc change in glaucoma. Ophthalmology. 2009;116:2110–8.
pubmed: 19500850
doi: 10.1016/j.ophtha.2009.04.031
Medeiros FA, Alencar LM, Zangwill LM, Bowd C, Sample PA, Weinreb RN. Prediction of functional loss in glaucoma from progressive optic disc damage. Arch Ophthalmol. 2009;127:1250–6.
pubmed: 19822839
pmcid: 2828324
doi: 10.1001/archophthalmol.2009.276
Miki A, Medeiros FA, Weinreb RN, Jain S, He F, Sharpsten L, et al. Rates of retinal nerve fiber layer thinning in glaucoma suspect eyes. Ophthalmology. 2014;121:1350–8.
pubmed: 24629619
doi: 10.1016/j.ophtha.2014.01.017
Wadhwani M, Bali SJ, Satyapal R, Angmo D, Sharma R, Pandey V, et al. Test-retest variability of retinal nerve fiber layer thickness and macular ganglion cell-inner plexiform layer thickness measurements using spectral-domain optical coherence tomography. J Glaucoma. 2015;24:e109–15.
pubmed: 25517254
doi: 10.1097/IJG.0000000000000203
Francoz M, Fenolland JR, Giraud JM, El Chehab H, Sendon D, May F, et al. Reproducibility of macular ganglion cell-inner plexiform layer thickness measurement with cirrus HD-OCT in normal, hypertensive and glaucomatous eyes. Br J Ophthalmol. 2014;98:322–8.
pubmed: 24307717
doi: 10.1136/bjophthalmol-2012-302242
Mwanza JC, Oakley JD, Budenz DL, Chang RT, Knight OJ, Feuer WJ. Macular ganglion cell-inner plexiform layer: automated detection and thickness reproducibility with spectral domain-optical coherence tomography in glaucoma. Invest Ophthalmol Vis Sci. 2011;52:8323–9.
pubmed: 21917932
pmcid: 3208140
doi: 10.1167/iovs.11-7962
Ghasia FF, El-Dairi M, Freedman SF, Rajani A, Asrani S. Reproducibility of spectral-domain optical coherence tomography measurements in adult and pediatric glaucoma. J Glaucoma. 2015;24:55–63.
pubmed: 23722865
doi: 10.1097/IJG.0b013e31829521db
Garway-Heath DF, Zhu H, Cheng Q, Morgan K, Frost C, Crabb DP, et al. Combining optical coherence tomography with visual field data to rapidly detect disease progression in glaucoma: a diagnostic accuracy study. Health Technol Assess. 2018;22:1–106.
pubmed: 29384083
pmcid: 5817413
doi: 10.3310/hta22040
Zheng Y, Xu L, Kiwaki T, Wang J, Murata H, Asaoka R et al. Glaucoma Progression Prediction Using Retinal Thickness via Latent Space Linear Regression. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). 2019: 2278-86.
Zhang Y, Yang Q. A survey on multi-task learning. Natl Sci Rev. 2018;5:30–43.
doi: 10.1093/nsr/nwx105
Ruder R. An overview of multi-task learning in deep neural networks. Preprint. Posted online Jun 15, 2017. arXiv:1706.05098. doi: https://doi.org/10.48550/arXiv.1706.05098 .
Hashimoto Y, Asaoka R, Kiwaki T, Sugiura H, Asano S, Murata H, et al. Deep learning model to predict visual field in central 10 degrees from optical coherence tomography measurement in glaucoma. Br J Ophthalmol. 2021;105:507–13.
pubmed: 32593978
doi: 10.1136/bjophthalmol-2019-315600
Hashimoto Y, Kiwaki T, Sugiura H, Asano S, Murata H, Fujino Y, et al. Predicting 10 – 2 visual field from optical coherence tomography in Glaucoma using deep learning corrected with 24 – 2/30 – 2 visual field. Transl Vis Sci Technol. 2021;10:28.
pubmed: 34812893
pmcid: 8626848
doi: 10.1167/tvst.10.13.28
Xu L, Asaoka R, Kiwaki T, Murata H, Fujino Y, Matsuura M, et al. Predicting the Glaucomatous Central 10-Degree Visual Field from Optical Coherence Tomography using Deep Learning and Tensor Regression. Am J Ophthalmol. 2020;218:304–13.
pubmed: 32387432
doi: 10.1016/j.ajo.2020.04.037
Asano S, Asaoka R, Murata H, Hashimoto Y, Miki A, Mori K, et al. Predicting the central 10 degrees visual field in glaucoma by applying a deep learning algorithm to optical coherence tomography images. Sci Rep. 2021;11:2214.
pubmed: 33500462
pmcid: 7838164
doi: 10.1038/s41598-020-79494-6
Hood DC, Kardon RH. A framework for comparing structural and functional measures of glaucomatous damage. Prog Retin Eye Res. 2007;26:688–710.
pubmed: 17889587
pmcid: 2110881
doi: 10.1016/j.preteyeres.2007.08.001
Asaoka R, Xu L, Murata H, Kiwaki T, Matsuura M, Fujino Y, et al. A joint Multitask Learning Model for cross-sectional and longitudinal predictions of Visual Field using OCT. Ophthalmol Sci. 2021;1:100055.
pubmed: 36246943
pmcid: 9560642
doi: 10.1016/j.xops.2021.100055
Detry-Morel M, Jamart J, Hautenauven F, Pourjavan S. Comparison of the corneal biomechanical properties with the Ocular Response Analyzer(R) (ORA) in african and caucasian normal subjects and patients with glaucoma. Acta Ophthalmol. 2012;90:e118–24.
pubmed: 21989354
doi: 10.1111/j.1755-3768.2011.02274.x
Susanna CN, Diniz-Filho A, Daga FB, Susanna BN, Zhu F, Ogata NG, et al. A prospective longitudinal study to investigate corneal hysteresis as a risk factor for Predicting Development of Glaucoma. Am J Ophthalmol. 2018;187:148–52.
pubmed: 29305310
pmcid: 6884090
doi: 10.1016/j.ajo.2017.12.018
Hirasawa K, Matsuura M, Murata H, Nakakura S, Nakao Y, Kiuchi Y, et al. Association between corneal Biomechanical Properties with Ocular Response Analyzer and also CorvisST Tonometry, and Glaucomatous Visual Field Severity. Transl Vis Sci Technol. 2017;6:18.
pubmed: 28626602
pmcid: 5472364
doi: 10.1167/tvst.6.3.18
Medeiros FA, Meira-Freitas D, Lisboa R, Kuang TM, Zangwill LM, Weinreb RN. Corneal hysteresis as a risk factor for glaucoma progression: a prospective longitudinal study. Ophthalmology. 2013;120:1533–40.
pubmed: 23642371
doi: 10.1016/j.ophtha.2013.01.032
De Moraes CV, Hill V, Tello C, Liebmann JM, Ritch R. Lower corneal hysteresis is associated with more rapid glaucomatous visual field progression. J Glaucoma. 2012;21:209–13.
pubmed: 21654511
doi: 10.1097/IJG.0b013e3182071b92
Matsuura M, Hirasawa K, Murata H, Nakakura S, Kiuchi Y, Asaoka R. The usefulness of CorvisST Tonometry and the Ocular Response Analyzer to assess the progression of glaucoma. Sci Rep. 2017;7:40798.
pubmed: 28094315
pmcid: 5240132
doi: 10.1038/srep40798
Wolpert DH, Macready WG. No free lunch theorems for optimization. IEEE Trans Evol Comput. 1997;1:67.
doi: 10.1109/4235.585893