Progression of patterns (POP): A machine classifier algorithm to identify glaucoma progression in visual fields

Investigative Ophthalmology and Visual Science

Volumn 53, Issue 10, 2012, Pages 6557-6567

  Goldbaum, Michael H ^a   Lee, Intae ^a   Jang, Giljin ^a   Balasubramanian, Madhusudhanan ^a   Sample, Pamela A ^a   Weinreb, Robert N ^a   Liebmann, Jeffrey M ^b   Girkin, Christopher A ^c   Anderson, Douglas R ^d   Zangwill, Linda M ^a   Fredette, Marie Josee ^d,e   Jung, Tzyy Ping ^a   Medeiros, Felipe A ^a   Bowd, Christopher ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

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

^c UNIVERSITY OF ALABAMA AT BIRMINGHAM   (United States)

^d UNIVERSITY OF MIAMI MILLER SCHOOL OF MEDICINE   (United States)

^e UNIVERSITÉ LAVAL   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ADULT; AGED; ARTICLE; DISEASE COURSE; DISEASE SEVERITY; EYE EXAMINATION; GLAUCOMA; HUMAN; INTRAOCULAR PRESSURE; MACHINE LEARNING; MAJOR CLINICAL STUDY; NERVE FIBER; OPTIC DISK; OPTIC NERVE DISEASE; PERIMETRY; PRIORITY JOURNAL; RETINA GANGLION CELL; VISUAL FIELD DEFECT; ALGORITHM; ARTIFICIAL INTELLIGENCE; CLASSIFICATION; COMPUTER ASSISTED DIAGNOSIS; GONIOSCOPY; MIDDLE AGED; PATHOLOGY; PHYSIOLOGY; VISUAL ACUITY; VISUAL DISORDER; VISUAL FIELD;

AGED; ALGORITHMS; ARTIFICIAL INTELLIGENCE; DISEASE PROGRESSION; GLAUCOMA; GONIOSCOPY; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; INTRAOCULAR PRESSURE; MIDDLE AGED; NERVE FIBERS; OPTIC DISK; OPTIC NERVE DISEASES; RETINAL GANGLION CELLS; VISION DISORDERS; VISUAL ACUITY; VISUAL FIELD TESTS; VISUAL FIELDS;

EID: 84871889730     PISSN: 01460404     EISSN: 15525783     Source Type: Journal    
DOI: 10.1167/iovs.11-8363     Document Type: Article

References (20)

1. Goldberg I. How common is glaucoma worldwide? In: Weinreb RN, Kitazawa Y, Krieglstein G, ed. Glaucoma in the 21st Century. London, England: Mosby International; 2000: 3-8.
2. Quigley HA. Number of people with glaucoma worldwide. Br J Ophthalmol. 1996;80:389-393.
3. Flammer J, Drance SM, Fankhauser F, Augustiny L. Differential light threshold in automated static perimetry. Factors influencing short-term fluctuation. Arch Ophthalmol. 1984;102: 876-879.
4. 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;102:704-706.
5. Heijl A, Lindgren A, Lindgren G. Test-retest variability in glaucomatous visual fields. Am J Ophthalmol. 1989;108:130-135.
6. Lewis RA, Johnson CA, Keltner JL, Labermeier PK. Variability of quantitative automated perimetry in normal observers. Ophthalmology. 1986;93:878-881.
7. Wilensky JT, Joondeph BC. Variation in visual field measurements with an automated perimeter. Am J Ophthalmol. 1984; 97:328-331.
8. Bengtsson B, Heijl A. A visual field index for calculation of glaucoma rate of progression. Am J Ophthalmol. 2008;145: 343-353.
9. Leske MC, Heijl A, Hyman L, Bengtsson B. Early Manifest Glaucoma Trial: design and baseline data. Ophthalmology. 1999;106:2144-2153.
10. Goldbaum MH. Unsupervised learning with independent component analysis can identify patterns of glaucomatous visual field defects. Trans Am Ophthalmol Soc. 2005;103:270-280.
11. Goldbaum MH, Jang GJ, Bowd C, et al. Patterns of glaucomatous visual field loss in sita fields automatically identified using independent component analysis. Trans Am Ophthalmol Soc. 2009;107:136-144.
12. Gordon MO, Kass MA. The Ocular Hypertension Treatment Study: design and baseline description of the participants. Arch Ophthalmol. 1999;117:573-583.
13. Sample PA, Girkin CA, Zangwill LM, et al. The African Descent and Glaucoma Evaluation Study (ADAGES): design and baseline data. Arch Ophthalmol. 2009;127:1136-1145.
14. Racette L, Liebmann JM, Girkin CA, et al. African Descent and Glaucoma Evaluation Study (ADAGES): III. Ancestry differences in visual function in healthy eyes. Arch Ophthalmol. 2010; 128:551-559.
15. 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.
16. Chauhan BC, McCormick TA, Nicolela MT, LeBlanc RP. Optic disc and visual field changes in a prospective longitudinal study of patients with glaucoma: comparison of scanning laser tomography with conventional perimetry and optic disc photography. Arch Ophthalmol. 2001;119:1492-1499.
17. Chauhan BC, Nicolela MT, Artes PH. Incidence and rates of visual field progression after longitudinally measured optic disc change in glaucoma. Ophthalmology. 2009;116:2110-2118.
18. Strouthidis NG, Scott A, Peter NM, Garway-Heath DF. Optic disc and visual field progression in ocular hypertensive subjects: detection rates, specificity, and agreement. Invest Ophthalmol Vis Sci. 2006;47:2904-2910.
19. Xin D, Greenstein VC, Ritch R, Liebmann JM, De Moraes CG, Hood DC. A comparison of functional and structural measures for identifying progression of glaucoma. Invest Ophthalmol Vis Sci. 2011;52:519-526.
20. Tversky A, Kahneman D. Judgment under uncertainty: heuristics and biases. Science. 1974;185:1124-1131.