Patterns of functional vision loss in glaucoma determined with archetypal analysis

Journal of the Royal Society Interface

Volumn 12, Issue 103, 2015, Pages

  Elze, Tobias ^a,b   Pasquale, Louis R ^c,d   Shen, Lucy Q ^c   Chen, Teresa C ^c   Wiggs, Janey L ^c   Bex, Peter J ^a  

^a SCHEPENS EYE RESEARCH INSTITUTE   (United States)

^b MAX PLANCK INSTITUTE FOR MATHEMATICS IN THE SCIENCES   (Germany)

^c HARVARD MEDICAL SCHOOL   (United States)

^d BRIGHAM AND WOMEN S HOSPITAL   (United States)

Author keywords

Glaucoma; Retinal nerve fibre layer; Vision loss

Indexed keywords

ALDEHYDES; VISION;

ARCHETYPAL ANALYSIS; CLASSIFICATION SCHEME; COMPONENT BASED APPROACH; GLAUCOMA; OPTIC NEUROPATHIES; PROTOTYPICAL PATTERNS; RETINAL NERVES; VISION LOSS;

OPHTHALMOLOGY;

ALGORITHM; ARCHETYPAL ANALYSIS; ARTICLE; CLUSTER ANALYSIS; FACTORIAL ANALYSIS; FUNCTIONAL VISION LOSS; GLAUCOMA; HUMAN; INDEPENDENT COMPONENT ANALYSIS; INTRAOCULAR HYPERTENSION; LEARNING; MATHEMATICAL MODEL; MAXIMUM LIKELIHOOD METHOD; MEDICAL DECISION MAKING; OPTIC NERVE FIBER; PRINCIPAL COMPONENT ANALYSIS; QUALITATIVE ANALYSIS; RETROSPECTIVE STUDY; STATISTICAL ANALYSIS; VISUAL FIELD; VISUAL IMPAIRMENT; BIOLOGICAL MODEL; BLINDNESS; CLASSIFICATION; COMPLICATION; PATHOPHYSIOLOGY; RETINA NERVE CELL;

BLINDNESS; GLAUCOMA; HUMANS; MODELS, BIOLOGICAL; RETINAL NEURONS;

EID: 85005926424     PISSN: 17425689     EISSN: 17425662     Source Type: Journal    
DOI: 10.1098/rsif.2014.1118     Document Type: Article

References (35)

1. Quigley HA. 2011 Glaucoma. Lancet 377, 1367- 1377. (doi:10.1016/S0140-6736(10)61423-7).
2. Brusini P, Johnson CA. 2007 Staging functional damage in glaucoma: review of different classification methods. Surv. Ophthalmol. 52, 156-179. (doi:10.1016/j.survophthal.2006.12.008).
3. Wiggs JL et al. 2012 The p53 codon 72 PRO/PRO genotype may be associated with initial central visual field defects in Caucasians with primary open angle glaucoma. PLoS ONE 7, e45613. (doi:10.1371/journal.pone.0045613).
4. Loomis SJ et al. 2014 Association of cav1/cav2 genomic variants with primary open-angle glaucoma overall and by gender and pattern of visual field loss. Ophthalmology 121, 508-516. (doi:10.1016/j.ophtha.2013.09.012).
5. Buys ES et al. 2013 Soluble guanylate cyclase 1-deficient mice: A novel murine model for primary open angle glaucoma. PLoS ONE 8, e60156. (doi:10. 1371/journal.pone.0060156).
6. Boland MV, Quigley HA. 2007 Risk factors and open-angle glaucoma: classification and application. J. Glaucoma 16, 406-418. (doi:10.1097/IJG. 0b013e31806540a1).
7. Keltner JL, Johnson CA, Cello KE, Edwards MA, Bandermann SE, Kass MA, Gordon MO. 2003 Classification of visual field abnormalities in the ocular hypertension treatment study. Arch. Ophthalmol. 121, 643-650. (doi:10.1001/archopht. 121.5.643).
8. Hartigan JA, Wong MA. 1979 Algorithm AS 136: A k-means clustering algorithm. J. R. Stat. Soc. C 28, 100-108.
9. Henson DB, Spenceley SE, Bull DR. 1996 Spatial classification of glaucomatous visual field loss. Br. J. Ophthalmol. 80, 526-531. (doi:10.1136/bjo. 80.6.526).
10. Oden N. 1992 Morphology of the normal visual-field in a population-based random sample: principal components analysis. Stat. Med. 11, 1131-1150. (doi:10.1002/sim.4780110902).
11. Hilton S, Katz J, Zeger S. 1996 Classifying visual field data. Stat. Med. 15, 1349-1364. (doi:10.1002/(SICI)1097-0258(19960715)15:13, 1349::AIDSIM270.3.0. CO;2-B).
12. Thurstone LL. 1947 Multiple factor analysis. Chicago, IL: University of Chicago Press.
13. Hastie T, Tibshirani R, Friedman J. 2009 The elements of statistical learning. New York, NY: Springer.
14. Hyvärinen A, Karhunen J, Oja E. 2001 Independent component analysis. New York, NY: Wiley.
15. Artes PH, Iwase A, Ohno Y, Kitazawa Y, Chauhan BC. 2002 Properties of perimetric threshold estimates from full threshold, SITA standard, and SITA fast strategies. Invest. Ophthalmol. Vis. Sci. 43, 2654-2659.
16. Murphy KP. 2012 Machine learning. A probabilistic perspective. Cambridge, MA: MIT Press.
17. Sample PA 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. (doi:10.1167/iovs.03-0343).
18. Goldbaum MH 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. (doi:10.1167/iovs.04-1167).
19. Goldbaum MH et al. 2009 Patterns of glaucomatous visual field loss in SITA fields automatically identified using independent component analysis. Trans. Am. Ophthalmol. Soc. 107, 136-145.
20. Chan K, Lee TW, Sejnowski TJ. 2002 Variational learning of clusters of under complete non symmetric independent components. J. Mach. Learn. Res. 3, 99-114.
21. Cutler A, Breiman L. 1994 Archetypal analysis. Technometrics 36, 338-347. (doi:10.1080/00401706.1994.10485840).
22. Bengtsson B, Olsson J, Heijl A, Rootzen H. 1997 A new generation of algorithms for computerized threshold perimetry, SITA. Acta Ophthalmol. Scand. 75, 368-375. (doi:10.1111/j.1600-0420.1997. tb00392.x).
23. Bickler-Bluth M, Trick GL, Kolker AE, Cooper DG. 1989 Assessing the utility of reliability indexes for automated visual-fields: Testing ocular hyper tensives. Ophthalmology 96, 616-619. (doi:10.1016/S0161-6420(89)32840-5).
24. Heijl A, Lindgren A, Olsson J. 1987 A package for the statistical analysis of visual fields. Doc. Ophthalmol. Proc. Ser. 49, 153-168. (doi:10.1007/978-94-009-3325-5-23).
25. Heijl A, Lindgren G, Olsson J. 1987 Normal variability of static perimetric threshold values across the central visual field. Arch. Ophthalmol. 105, 1544-1549. (doi:10.1001/archopht.1987. 01060110090039).
26. R Core Team. 2013 R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.
27. Eugster MJA, Leisch F. 2009 From spider-man to hero: Archetypal analysis in R. J. Stat. Soft. 30, 1-23.
28. Jansonius NM et al. 2009 A mathematical description of nerve fiber bundle trajectories and their variability in the human retina. Vision Res. 49, 2157-2163. (doi:10.1016/j.visres.2009.04.029).
29. Jansonius NM, Schiefer J, Nevalainen J, Paetzold J, Schiefer U. 2012 A mathematical model for describing the retinal nerve fiber bundle trajectories in the human eye: Average course, variability, and influence of refraction, optic disc size and optic disc position. Exp. Eye Res. 105, 70-78. (doi:10.1016/j. exer.2012.10.008).
30. Azzalini A. 1985 A class of distributions which includes the normal ones. Scand. J. Stat. 12, 171-178.
31. Hood DC, Kardon RH. 2007 A framework for comparing structural and functional measures of glaucomatous damage. Prog. Retin. Eye Res. 26, 688-710. (doi:10.1016/j.preteyeres.2007.08.001).
32. Katz J, Sommer A, Gaasterland DE, Anderson DR. 1991 Comparison of analytic algorithms for detecting glaucomatous visual-field loss. Arch. Ophthalmol. 109, 1684-1689. (doi:10.1001/archopht.1991.01080120068028).
33. Hood DC, Raza AS, De Moraes CGV, Liebmann JM, Ritch R. 2013 Glaucomatous damage of the macula. Prog. Retin. Eye Res. 32, 1-21. (doi:10.1016/j. preteyeres.2012.08.003).
34. Heijl A, Lindgren G, Lindgren A, Olsson J, Asman P, Myers S, Patella M. 1991 Extended empirical statistical package for evaluation of single and multiple fields in glaucoma: Statpac 2. In Perimetry update 1990/1991 (eds RP Mills, A Heijl), pp. 303-315. Amsterdam, The Netherlands: Kugler.
35. Denniss J, Mckendrick AM, Turpin A. 2012 An anatomically customizable computational model relating the visual field to the optic nerve head in individual eyes. Invest. Ophthalmol. Vis. Sci. 53, 6981-6990. (doi:10.1167/iovs.12-9657).