Patterns of glaucomatous visual field loss in sita fields automatically identified using independent component analysis

Transactions of the American Ophthalmological Society

Volumn 107, Issue , 2009, Pages 136-144

  Goldbaum, Michael H ^a   Jang, Gil Jin ^b   Bowd, Chris ^a   Hao, Jiucang ^b   Zangwill, Linda M ^a   Liebmann, Jeffrey ^c   Girkin, Christopher ^d   Jung, Tzyy Ping ^b   Weinreb, Robert N ^a   Sample, Pamela A ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UNIVERSITY OF CALIFORNIA SAN DIEGO   (United States)

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

^d UNIVERSITY OF ALABAMA AT BIRMINGHAM   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ANALYZER; ARTICLE; BAYES THEOREM; COMPARATIVE STUDY; CONTROLLED STUDY; DIAGNOSTIC ACCURACY; DIAGNOSTIC VALUE; DISEASE SEVERITY; EYE; GLAUCOMA; HUMAN; INDEPENDENT COMPONENT ANALYSIS; MAJOR CLINICAL STUDY; MODEL; PRIORITY JOURNAL; VISUAL FIELD; VISUAL FIELD DEFECT;

ALGORITHMS; BAYES THEOREM; DIAGNOSTIC TECHNIQUES, OPHTHALMOLOGICAL; GLAUCOMA; HUMANS; MODELS, STATISTICAL; SENSITIVITY AND SPECIFICITY; SEVERITY OF ILLNESS INDEX; VISUAL FIELDS;

EID: 77949394244     PISSN: None     EISSN: 15456110     Source Type: Journal    
DOI: None     Document Type: Article

References (11)

1. Goldbaum MH. Unsupervised learning with independent component analysis can identify patterns of glaucomatous visual field defects. Trans Am Ophthalmol Soc 2005;103:270-280.
2. Goldbaum MH, Sample PA, Zhang Z, et al. Using unsupervised learning with independent component analysis to identify patterns of glaucomatous visual field defects. Invest Ophthalmol Vis Sci 2005;46:3676-3683.
3. Sample PA, Boden C, Zhang Z, 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.
4. Sample PA, Chan K-L, Boden C, et al. Using unsupervised learning with variational Bayesian mixture of factor analysis to identify patterns of glaucomatous visual field defects. Invest Ophthalmol Vis Sci 2004;45:2596-2605.
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7. Lee T-W, Lewicki MS, Sejnowski TJ. ICA mixture models for unsupervised classification of non-Gaussian sources and automatic context switching in blind signal separation. IEEE Trans Pattern Anal Mach Intell 2000;22:1078-1089.
8. Sample PA, Jang G, Jung T-P, et al. Unsupervised machine learning with independent component analysis identifies patterns of glaucomatous visual field loss in SITA fields. Invest Ophthalmol Vis Sci 2009;50:E-Abstract 5283.
9. 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.
10. Goldbaum MH, Sample PA, Chan K-L, et al. Comparing machine learning classifiers for diagnosing glaucoma from standard automated perimetry. Invest Ophthalmol Vis Sci 2002;43:162-169.
11. Sample PA, Goldbaum MH, Chan K-L, et al. Using machine learning classifiers to identify glaucomatous change earlier in standard visual fields. Invest Ophthalmol Vis Sci 2002;43:2660-2665.