Glaucomatous patterns in Frequency Doubling Technology (FDT) perimetry data identified by unsupervised machine learning classifiers

PLoS ONE

Volumn 9, Issue 1, 2014, Pages

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

^a UNIVERSITY OF CALIFORNIA   (United States)

^b Ulsan National Institute of Science and Technology   (South Korea)

^c UNIVERSITY OF ALABAMA AT BIRMINGHAM   (United States)

^d NEW YORK UNIVERSITY SCHOOL OF MEDICINE   (United States)

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

Author keywords

[No Author keywords available]

Indexed keywords

ADULT; AGE; ARTICLE; CLASSIFIER; CONTROLLED STUDY; DISEASE SEVERITY; FALSE NEGATIVE RESULT; FALSE POSITIVE RESULT; FREQUENCY DOUBLING TECHNOLOGY PERIMETRY; GLAUCOMA; HUMAN; INFORMATION PROCESSING; MAJOR CLINICAL STUDY; MIDDLE AGED; PERIMETRY; SENSITIVITY AND SPECIFICITY; VARIATIONAL BAYESIAN INDEPENDENT COMPONENT ANALYSIS MIXTURE; VISUAL FIELD DEFECT; AGED; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; BIOLOGICAL MODEL; CASE CONTROL STUDY; CLUSTER ANALYSIS; COMPUTER SIMULATION;

ADULT; AGED; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; CASE-CONTROL STUDIES; CLUSTER ANALYSIS; COMPUTER SIMULATION; GLAUCOMA; HUMANS; MIDDLE AGED; MODELS, BIOLOGICAL; SENSITIVITY AND SPECIFICITY;

EID: 84900432666     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0085941     Document Type: Article

References (46)

1. Goldbaum MH, Sample PA, White H, Colt B, Raphaelian P, et al. (1994) Interpretation of automated perimetry for glaucoma by neural network. Invest Ophthalmol Vis Sci 35: 3362-3373. (Pubitemid 24337941)
2. Spenceley SE, Henson DB, Bull DR (1994) Visual field analysis using artificial neural networks. Ophthalmic Physiol Opt 14: 239-248. (Pubitemid 24241437)
3. Brigatti L, Hoffman D, Caprioli J (1996) Neural networks to identify glaucoma with structural and functional measurements. Am J Ophthalmol 121: 511-521. (Pubitemid 26135859)
4. Uchida H, Brigatti L, Caprioli J (1996) Detection of structural damage from glaucoma with confocal laser image analysis. Invest Ophthalmol Vis Sci 37: 2393-2401. (Pubitemid 26391277)
5. Lietman T, Eng J, Katz J, Quigley HA (1999) Neural networks for visual field analysis: how do they compare with other algorithms? J Glaucoma 8: 77-80.
6. Bowd C, Chan K, Zangwill LM, Goldbaum MH, Lee TW, et al. (2002) Comparing neural networks and linear discriminant functions for glaucoma detection using confocal scanning laser ophthalmoscopy of the optic disc. Invest Ophthalmol Vis Sci 43: 3444-3454. (Pubitemid 35334352)
7. Chan K, Lee TW, Sample PA, Goldbaum MH, Weinreb RN, et al. (2002) Comparison of machine learning and traditional classifiers in glaucoma diagnosis. IEEE Trans Biomed Eng 49: 963-974. (Pubitemid 34925465)
8. Goldbaum MH, Sample PA, Chan K, Williams J, Lee TW, et al. (2002) Comparing machine learning classifiers for diagnosing glaucoma from standard automated perimetry. Invest Ophthalmol Vis Sci 43: 162-169. (Pubitemid 34032455)
9. Hothorn T, Lausen B (2003) Bagging tree classifiers for laser scanning images: a data- and simulation-based strategy. Artif Intell Med 27: 65-79. (Pubitemid 35418291)
10. Zangwill LM, Chan K, Bowd C, Hao J, Lee TW, et al. (2004) Heidelberg retina tomograph measurements of the optic disc and parapapillary retina for detecting glaucoma analyzed by machine learning classifiers. Invest Ophthalmol Vis Sci 45: 3144-3151. (Pubitemid 39121848)
11. Bengtsson B, Bizios D, Heijl A (2005) Effects of input data on the performance of a neural network in distinguishing normal and glaucomatous visual fields. Invest Ophthalmol Vis Sci 46: 3730-3736. (Pubitemid 44264679)
12. Bowd C, Medeiros FA, Zhang Z, Zangwill LM, Hao J, et al. (2005) Relevance vector machine and support vector machine classifier analysis of scanning laser polarimetry retinal nerve fiber layer measurements. Invest Ophthalmol Vis Sci 46: 1322-1329. (Pubitemid 41685872)
13. Burgansky-Eliash Z, Wollstein G, Chu T, Ramsey JD, Glymour C, et al. (2005) Optical coherence tomography machine learning classifiers for glaucoma detection: a preliminary study. Invest Ophthalmol Vis Sci 46: 4147-4152. (Pubitemid 44264616)
14. Bowd C, Chiou C, Hao J, Racette L, Zangwill L, et al. (2006) Relevance vector machine for combining HRT II and SWAP results for discriminating between healthy and glaucoma eyes. Acta Ophthalmol Scand (Abstracts) 84: 569.
15. Bizios D, Heijl A, Bengtsson B (2007) Trained artificial neural network for glaucoma diagnosis using visual field data: a comparison with conventional algorithms. J Glaucoma 16: 20-28. (Pubitemid 46114769)
16. Bowd C, Hao J, Tavares IM, Medeiros FA, Zangwill LM, et al. (2008) Bayesian machine learning classifiers for combining structural and functional measurements to classify healthy and glaucomatous eyes. Invest Ophthalmol Vis Sci 49: 945-953.
17. Grewal DS, Jain R, Grewal SP, Rihani V (2008) Artificial neural network-based glaucoma diagnosis using retinal nerve fiber layer analysis. Eur J Ophthalmol 18: 915-921.
18. Townsend KA, Wollstein G, Danks D, Sung KR, Ishikawa H, et al. (2008) Heidelberg Retina Tomograph 3 machine learning classifiers for glaucoma detection. Br J Ophthalmol 92: 814-818. (Pubitemid 351839575)
19. Racette L, Chiou CY, Hao J, Bowd C, Goldbaum MH, et al. (2010) Combining functional and structural tests improves the diagnostic accuracy of relevance vector machine classifiers. J Glaucoma 19: 167-175.
20. Wroblewski D, Francis BA, Chopra V, Kawji AS, Quiros P, et al. (2009) Glaucoma detection and evaluation through pattern recognition in standard automated perimetry data. Graefes Arch Clin Exp Ophthalmol 247: 1517-1530.
21. Bowd C, Lee I, Goldbaum MH, Balasubramanian M, Medeiros FA, et al. (2012) Predicting glaucomatous progression in glaucoma suspect eyes using relevance vector machine classifiers for combined structural and functional measurements. Invest Ophthalmol Vis Sci 53: 2382-2389.
22. Sample PA, Chan K, Boden C, Lee TW, Blumenthal EZ, et al. (2004) Using unsupervised learning with variational bayesian mixture of factor analysis to identify patterns of glaucomatous visual field defects. Invest Ophthalmol Vis Sci 45: 2596-2605. (Pubitemid 38998855)
23. Goldbaum MH (2005) Unsupervised learning with independent component analysis can identify patterns of glaucomatous visual field defects. Trans Am Ophthalmol Soc 103: 270-280.
24. Goldbaum MH, Sample PA, Zhang Z, Chan K, Hao J, et al. (2005) Using unsupervised learning with independent component analysis to identify patterns of glaucomatous visual field defects. Invest Ophthalmol Vis Sci 46: 3676-3683. (Pubitemid 44264672)
25. Lee TW, Lewicki MS, Sejnowski TJ (2000) ICA mixture models for unsupervised classification of non-Gaussian classes and automatic context switching in blind signal separation. IEEE Trans PAMI 22: 1078-1089. (Pubitemid 32066976)
26. Kiviluoto K, Oja E (1998) Independent component analysis for parallel financial time series; Tokyo, Japan. 895-898.
27. Makeig S, Jung TP, Sejnowski TJ (1996) Independent Component Analysis of Electroencephalographic Data. Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press. 145-151.
28. Skillicorn D (2006) Social network analysis via matrix decompositions. In: Popp R, Yen J, editors. Emergent Information Technologies and Enabling Policies for Counter-Terrorism: IEEE, Inc. 331-347.
29. McKay D (1995) Probable networks and plausible predictions - A review of practical Bayesian methods for supervised neural netwrks. Network: Computation in Neural Systems. 469-505.
30. Sample PA, Bosworth CF, Blumenthal EZ, Girkin C, Weinreb RN (2000) Visual function-specific perimetry for indirect comparison of different ganglion cell populations in glaucoma. Invest Ophthalmol Vis Sci 41: 1783-1790. (Pubitemid 30354350)
31. Goldbaum MH, Lee I, Jang G, Balasubramanian M, Sample PA, et al. (2012) Progression of patterns (POP): A machine classifier algorithm to identify glaucoma progression in visual fields. Invest Ophthalmol Vis Sci 53: 6557-6567.
32. Turpin A, McKendrick AM, Johnson CA, Vingrys AJ (2002) Performance of efficient test procedures for frequency-doubling technology perimetry in normal and glaucomatous eyes. Invest Ophthalmol Vis Sci 43: 709-715. (Pubitemid 34184488)
33. Turpin A, McKendrick AM, Johnson CA, Vingrys AJ (2002) Development of efficient threshold strategies for frequency doubling technology perimetry using computer simulation. Invest Ophthalmol Vis Sci 43: 322-331. (Pubitemid 34113211)
34. Wall M, Woodward KR, Doyle CK, Artes PH (2009) Repeatability of automated perimetry: a comparison between standard automated perimetry with stimulus size III and V, matrix, and motion perimetry. Invest Ophthalmol Vis Sci 50: 974-979.
35. Racette L, Medeiros FA, Zangwill LM, Ng D, Weinreb RN, et al. (2008) Diagnostic accuracy of the Matrix 24-2 and original N-30 frequency-doubling technology tests compared with standard automated perimetry. Invest Ophthalmol Vis Sci 49: 954-960.
36. Chan K, Lee TW, Sejnowski T (2002) Variational learning of clusters of under complete nonsymmetric independent components. J Mach Learn Res 3: 99-114.
37. Choudrey RA, Roberts SJ (2003) Variational mixture of Bayesian independent component analyzers. Neural Comput 15: 213-252. (Pubitemid 37049839)
38. Goldbaum MH, Jang GJ, Bowd C, Hao J, Zangwill LM, et al. (2009) Patterns of glaucomatous visual field loss in sita fields automatically identified using independent component analysis. Trans Am Ophthalmol Soc 107: 136-144.
39. Armaly MF (1971) Visual field defects in early open angle glaucoma. Trans Am Ophthalmol Soc 69: 147-162.
40. Drance SM (1969) The early field defects in glaucoma. Invest Ophthalmol 8: 84-91.
41. Drance SM (1972) The glaucomatous visual field. Br J Ophthalmol 56: 186-200.
42. Heijl A, Lundqvist L (1984) The frequency distribution of earliest glaucomatous visual field defects documented by automatic perimetry. Acta Ophthalmol (Copenh) 62: 658-664. (Pubitemid 14057306)
43. Keltner JL, Johnson CA, Cello KE, Edwards MA, Bandermann SE, et al. (2003) Classification of visual field abnormalities in the ocular hypertension treatment study. Arch Ophthalmol 121: 643-650. (Pubitemid 36554466)
44. Swanson WH, Sun H, Lee BB, Cao D (2011) Responses of primate retinal ganglion cells to perimetric stimuli. Invest Ophthalmol Vis Sci 52: 764-771.
45. Maddess T (2011) Frequency-doubling technology and parasol cells. Invest Ophthalmol Vis Sci 52: 3759; author reply 3759-3760.
46. De Moraes CG, Liebmann JM, Ritch R, Hood DC (2012) Understanding disparities among diagnostic technologies in glaucoma. Arch Ophthalmol 130: 833-840.