Predicting glaucomatous progression in glaucoma suspect eyes using relevance vector machine classifiers for combined structural and functional measurements

Investigative Ophthalmology and Visual Science

Volumn 53, Issue 4, 2012, Pages 2382-2389

  Bowd, Christopher ^a   Lee, Intae ^a   Goldbaum, Michael H ^a   Balasubramanian, Madhusudhanan ^a   Medeiros, Felipe A ^a   Zangwill, Linda M ^a   Girkin, Christopher A ^b   Liebmann, Jeffrey M ^c   Weinreb, Robert N ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UNIVERSITY OF ALABAMA AT BIRMINGHAM   (United States)

^c NEW YORK EYE AND EAR INFIRMARY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADULT; ARTICLE; CONTROLLED STUDY; DIAGNOSTIC ACCURACY; DISEASE COURSE; FEMALE; FUNCTION TEST; GLAUCOMA; HUMAN; MALE; PERIMETRY; PREDICTION; PRIORITY JOURNAL; PROBABILITY; RECEIVER OPERATING CHARACTERISTIC; RELEVANCE VECTOR MACHINE; SCANNING LASER OPHTHALMOSCOPY; SENSITIVITY AND SPECIFICITY; STRUCTURE ANALYSIS;

EID: 84861138923     PISSN: 01460404     EISSN: 15525783     Source Type: Journal    
DOI: 10.1167/iovs.11-7951     Document Type: Article

References (50)

1. Gordon MO, Beiser JA, Brandt JD, et al. The Ocular Hypertension Treatment Study: baseline factors that predict the onset of primary open-angle glaucoma. Arch Ophthalmol. 2002;120:714-720; discussion 829-730.
2. Miglior S, Pfeiffer N, Torri V, Zeyen T, Cunha-Vaz J, Adamsons I. Predictive factors for open-angle glaucoma among patients with ocular hypertension in the European Glaucoma Prevention Study. Ophthalmology. 2007;114:3-9.
3. Alencar LM, Bowd C, Weinreb RN, Zangwill LM, Sample PA, Medeiros FA. Comparison of HRT-3 glaucoma probability score and subjective stereophotograph assessment for prediction of progression in glaucoma. Invest Ophthalmol Vis Sci. 2008;49: 1898-1906.
4. Bowd C, Zangwill LM, Medeiros FA, et al. Confocal scanning laser ophthalmoscopy classifiers and stereophotograph evaluation for prediction of visual field abnormalities in glaucomasuspect eyes. Invest Ophthalmol Vis Sci. 2004;45:2255-2262.
5. Demirel S, Fortune B, Fan J, et al. Predicting progressive glaucomatous optic neuropathy using baseline standard automated perimetry data. Invest Ophthalmol Vis Sci. 2009; 50:674-680.
6. Essock EA, Gunvant P, Zheng Y, Garway-Heath DF, Kotecha A, Spratt A. Predicting visual field loss in ocular hypertensive patients using wavelet-fourier analysis of GDx scanning laser polarimetry. Optom Vis Sci. 2007;84:380-387.
7. Lalezary M, Medeiros FA, Weinreb RN, et al. Baseline optical coherence tomography predicts the development of glaucomatous change in glaucoma suspects. Am J Ophthalmol. 2006;142:576-582.
8. Medeiros FA, Sample PA, Weinreb RN. Frequency doubling technology perimetry abnormalities as predictors of glaucomatous visual field loss. Am J Ophthalmol. 2004;137:863-871.
9. Mohammadi K, Bowd C, Weinreb RN, Medeiros FA, Sample PA, Zangwill LM. Retinal nerve fiber layer thickness measurements with scanning laser polarimetry predict glaucomatous visual field loss. Am J Ophthalmol. 2004;138:592-601.
10. Strouthidis NG, Gardiner SK, Owen VM, Zuniga C, Garway-Heath DF. Predicting progression to glaucoma in ocular hypertensive patients. J Glaucoma. 2010;19:304-309.
11. Zangwill LM, Weinreb RN, Beiser JA, et al. Baseline topographic optic disc measurements are associated with the development of primary open-angle glaucoma: the Confocal Scanning Laser Ophthalmoscopy Ancillary Study to the Ocular Hypertension Treatment Study. Arch Ophthalmol. 2005;123: 1188-1197.
12. 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.
13. 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;43:3444-3454.
14. 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;49:945-953.
15. Burgansky-Eliash Z, Wollstein G, Chu T, et al. Optical coherence tomography machine learning classifiers for glaucoma detection: a preliminary study. Invest Ophthalmol Vis Sci. 2005;46:4147-4152.
16. Grewal DS, Jain R, Grewal SP, Rihani V. Artificial neural network-based glaucoma diagnosis using retinal nerve fiber layer analysis. Eur J Ophthalmol. 2008;18:915-921.
17. Huang ML, Chen HY. Development and comparison of automated classifiers for glaucoma diagnosis using Stratus optical coherence tomography. Invest Ophthalmol Vis Sci. 2005;46:4121-4129.
18. Poinoosawmy D, Tan JC, Bunce C, Hitchings RA. The ability of the GDx nerve fibre analyser neural network to diagnose glaucoma. Graefes Arch Clin Exp Ophthalmol. 2001;239:122-127.
19. Townsend KA, Wollstein G, Danks D, et al. Heidelberg Retina Tomograph 3 machine learning classifiers for glaucoma detection. Br J Ophthalmol. 2008;92:814-818.
20. 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;45:3144-3151.
21. 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.
22. 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.
23. Brigatti L, Hoffman D, Caprioli J. Neural networks to identify glaucoma with structural and functional measurements. Am J Ophthalmol. 1996;121:511-521.
24. 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.
25. Goldbaum MH, Sample PA, White H, et al. Interpretation of automated perimetry for glaucoma by neural network. Invest Ophthalmol Vis Sci. 1994;35:3362-3373.
26. Goldbaum MH, Sample PA, Chan K, et al. Comparing machine learning classifiers for diagnosing glaucoma from standard automated perimetry. Invest Ophthalmol Vis Sci. 2002;43: 162-169.
27. Henson DB, Spenceley SE, Bull DR. Spatial classification of glaucomatous visual field loss. Br J Ophthalmol. 1996;80:526-531.
28. Henson DB, Spenceley SE, Bull DR. Artificial neural network analysis of noisy visual field data in glaucoma. Artif Intell Med. 1997;10:99-113.
29. 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.
30. Mutlukan E, Keating D. Visual field interpretation with a personal computer based neural network. Eye. 1994;8(Pt 3); 321-323.
31. Spenceley SE, Henson DB, Bull DR. Visual field analysis using artificial neural networks. Ophthalmic Physiol Opt. 1994;14: 239-248.
32. Bowd C, Goldbaum MH. Machine learning classifiers in glaucoma. Optom Vis Sci. 2008;85:396-405.
33. Bishop CM, Tipping ME. Variational relevance vector machines. In: Boutilier C, Goldszmidt M (eds), Uncertainty in Artificial Intelligence. Cambridge, UK: Microsoft Research; 2000;45-63.
34. Tipping ME. Sparse Bayesian learning and the relevance vector machine. Journal of Machine Learning Research. 2001;1: 211-244.
35. 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;46:1322-1329.
36. Bowd C, Chiou C, Hao J, et al. Relevance vector machine for combining HRT II and SWAP results for discriminating between healthy and glaucoma eyes. Acta Ophthalmol Scand (Abstracts). 2006;84:569.
37. Goldbaum MH, Sample PA, Zangwill LM, et al. Probability of glaucoma determined from standard automated perimetry and from optic disk topography using relevance vector machine. Invest Ophthalmol Vis Sci. 2004;45:E-Abstract 2137.
38. Racette L, Chiou CY, Hao J, et al. Combining functional and structural tests improves the diagnostic accuracy of relevance vector machine classifiers. J Glaucoma. 2010;19:167-175.
39. Leske MC, Heijl A, Hyman L, Bengtsson B. Early Manifest Glaucoma Trial: design and baseline data. Ophthalmology. 1999;106:2144-2153.
40. Bishop C, Tipping M. Bayesian regression and classification. In: Suykens J, Horvarth G (eds), Advances in Learning Theory: Methods, Models and Applications. Amsterdam: IOS Press; 2003;267-285.
41. Zhou K, Li G. A unified approach to nonparametric comparison of receiver operating characteristic curves for longitudinal and clustered data. J Am Stat Assoc. 2008;103: 705-713.
42. Zhou K. R Code for Nonparametric Comparison of ROC curves from Clustered Data. Available at: https://sites.google.com/site/roccluster/. Accessed November 8, 2011.
43. Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 1982;143:29-36.
44. Zhang DD, Zhou XH, Freeman DH Jr, Freeman JL. A nonparametric method for the comparison of partial areas under ROC curves and its application to large health care data sets. Stat Med. 2002;21:701-715.
45. Jonas JB, Martus P, Horn FK, Junemann A, Korth M, Budde WM. Predictive factors of the optic nerve head for development or progression of glaucomatous visual field loss. Invest Ophthalmol Vis Sci. 2004;45:2613-2618.
46. 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: 1250-1256.
47. Bayer AU, Erb C. Short wavelength automated perimetry, frequency doubling technology perimetry, and pattern electroretinography for prediction of progressive glaucomatous standard visual field defects. Ophthalmology. 2002;109:1009-1017.
48. Breusegem C, Fieuws S, Stalmans I, Zeyen T. Agreement and accuracy of non-expert ophthalmologists in assessing glaucomatous changes in serial stereo optic disc photographs. Ophthalmology. 2011;118:742-746.
49. Jampel HD, Friedman D, Quigley H, 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.
50. Bowd C, Balasubramanian M, Weinreb RN, et al. Performance of confocal scanning laser tomograph Topographic Change Analysis (TCA) for assessing glaucomatous progression. Invest Ophthalmol Vis Sci. 2009;50:691-701.