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

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

Research output: Contribution to journalArticle

15 Citations (Scopus)

Abstract

Purpose. We evaluated Progression of Patterns (POP) for its ability to identify progression of glaucomatous visual field (VF) defects. Methods. POP uses variational Bayesian independent component mixture model (VIM), a machine learning classifier (MLC) developed previously. VIM separated Swedish Interactive Thresholding Algorithm (SITA) VFs from a set of 2,085 normal and glaucomatous eyes into nine axes (VF patterns): seven glaucomatous. Stable glaucoma was simulated in a second set of 55 patient eyes with five VFs each, collected within four weeks. A third set of 628 eyes with 4,186 VFs (mean ± SD of 6.7 ± 1.7 VFs over 4.0 ± 1.4 years) was tested for progression. Tested eyes were placed into suspect and glaucoma categories at baseline, based on VFs and disk stereoscopic photographs; a subset of eyes had stereophotographic evidence of progressive glaucomatous optic neuropathy (PGON). Each sequence of fields was projected along seven VIM glaucoma axes. Linear regression (LR) slopes generated from projections onto each axis yielded a degree of confidence (DOC) that there was progression. At 95% specificity, progression cutoffs were established for POP, visual field index (VFI), and mean deviation (MD). Guided progression analysis (GPA) was also compared. Results. POP identified a statistically similar number of eyes (P > 0.05) as progressing compared with VFI, MD, and GPA in suspects (3.8%, 2.7%, 5.6%, and 2.9%, respectively), and more eyes than GPA (P = 0.01) in glaucoma (16.0%, 15.3%, 12.0%, and 7.3%, respectively), and more eyes than GPA (P = 0.05) in PGON eyes (26.3%, 23.7%, 27.6%, and 14.5%, respectively). Conclusions. POP, with its display of DOC of progression and its identification of progressing VF defect pattern, adds to the information available to the clinician for detecting VF progression.

Original languageEnglish
Pages (from-to)6557-6567
Number of pages11
JournalInvestigative Ophthalmology and Visual Science
Volume53
Issue number10
DOIs
StatePublished - Sep 1 2012

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Visual Fields
Glaucoma
Optic Nerve Diseases
Ocular Hypertension
Aptitude
Linear Models

ASJC Scopus subject areas

  • Ophthalmology
  • Sensory Systems
  • Cellular and Molecular Neuroscience
  • Medicine(all)

Cite this

Goldbaum, M. H., Lee, I., Jang, G., Balasubramanian, M., Sample, P. A., Weinreb, R. N., ... Bowd, C. (2012). Progression of patterns (POP): A machine classifier algorithm to identify glaucoma progression in visual fields. Investigative Ophthalmology and Visual Science, 53(10), 6557-6567. https://doi.org/10.1167/iovs.11-8363

Progression of patterns (POP) : A machine classifier algorithm to identify glaucoma progression in visual fields. / Goldbaum, Michael H.; Lee, Intae; Jang, Giljin; Balasubramanian, Madhusudhanan; Sample, Pamela A.; Weinreb, Robert N.; Liebmann, Jeffrey M.; Girkin, Christopher A.; Anderson, Douglas; Zangwill, Linda M.; Fredette, Marie Josee; Jung, Tzyy Ping; Medeiros, Felipe A.; Bowd, Christopher.

In: Investigative Ophthalmology and Visual Science, Vol. 53, No. 10, 01.09.2012, p. 6557-6567.

Research output: Contribution to journalArticle

Goldbaum, MH, Lee, I, Jang, G, Balasubramanian, M, Sample, PA, Weinreb, RN, Liebmann, JM, Girkin, CA, Anderson, D, Zangwill, LM, Fredette, MJ, Jung, TP, Medeiros, FA & Bowd, C 2012, 'Progression of patterns (POP): A machine classifier algorithm to identify glaucoma progression in visual fields', Investigative Ophthalmology and Visual Science, vol. 53, no. 10, pp. 6557-6567. https://doi.org/10.1167/iovs.11-8363
Goldbaum, Michael H. ; Lee, Intae ; Jang, Giljin ; Balasubramanian, Madhusudhanan ; Sample, Pamela A. ; Weinreb, Robert N. ; Liebmann, Jeffrey M. ; Girkin, Christopher A. ; Anderson, Douglas ; Zangwill, Linda M. ; Fredette, Marie Josee ; Jung, Tzyy Ping ; Medeiros, Felipe A. ; Bowd, Christopher. / Progression of patterns (POP) : A machine classifier algorithm to identify glaucoma progression in visual fields. In: Investigative Ophthalmology and Visual Science. 2012 ; Vol. 53, No. 10. pp. 6557-6567.
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abstract = "Purpose. We evaluated Progression of Patterns (POP) for its ability to identify progression of glaucomatous visual field (VF) defects. Methods. POP uses variational Bayesian independent component mixture model (VIM), a machine learning classifier (MLC) developed previously. VIM separated Swedish Interactive Thresholding Algorithm (SITA) VFs from a set of 2,085 normal and glaucomatous eyes into nine axes (VF patterns): seven glaucomatous. Stable glaucoma was simulated in a second set of 55 patient eyes with five VFs each, collected within four weeks. A third set of 628 eyes with 4,186 VFs (mean ± SD of 6.7 ± 1.7 VFs over 4.0 ± 1.4 years) was tested for progression. Tested eyes were placed into suspect and glaucoma categories at baseline, based on VFs and disk stereoscopic photographs; a subset of eyes had stereophotographic evidence of progressive glaucomatous optic neuropathy (PGON). Each sequence of fields was projected along seven VIM glaucoma axes. Linear regression (LR) slopes generated from projections onto each axis yielded a degree of confidence (DOC) that there was progression. At 95{\%} specificity, progression cutoffs were established for POP, visual field index (VFI), and mean deviation (MD). Guided progression analysis (GPA) was also compared. Results. POP identified a statistically similar number of eyes (P > 0.05) as progressing compared with VFI, MD, and GPA in suspects (3.8{\%}, 2.7{\%}, 5.6{\%}, and 2.9{\%}, respectively), and more eyes than GPA (P = 0.01) in glaucoma (16.0{\%}, 15.3{\%}, 12.0{\%}, and 7.3{\%}, respectively), and more eyes than GPA (P = 0.05) in PGON eyes (26.3{\%}, 23.7{\%}, 27.6{\%}, and 14.5{\%}, respectively). Conclusions. POP, with its display of DOC of progression and its identification of progressing VF defect pattern, adds to the information available to the clinician for detecting VF progression.",
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AU - Lee, Intae

AU - Jang, Giljin

AU - Balasubramanian, Madhusudhanan

AU - Sample, Pamela A.

AU - Weinreb, Robert N.

AU - Liebmann, Jeffrey M.

AU - Girkin, Christopher A.

AU - Anderson, Douglas

AU - Zangwill, Linda M.

AU - Fredette, Marie Josee

AU - Jung, Tzyy Ping

AU - Medeiros, Felipe A.

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N2 - Purpose. We evaluated Progression of Patterns (POP) for its ability to identify progression of glaucomatous visual field (VF) defects. Methods. POP uses variational Bayesian independent component mixture model (VIM), a machine learning classifier (MLC) developed previously. VIM separated Swedish Interactive Thresholding Algorithm (SITA) VFs from a set of 2,085 normal and glaucomatous eyes into nine axes (VF patterns): seven glaucomatous. Stable glaucoma was simulated in a second set of 55 patient eyes with five VFs each, collected within four weeks. A third set of 628 eyes with 4,186 VFs (mean ± SD of 6.7 ± 1.7 VFs over 4.0 ± 1.4 years) was tested for progression. Tested eyes were placed into suspect and glaucoma categories at baseline, based on VFs and disk stereoscopic photographs; a subset of eyes had stereophotographic evidence of progressive glaucomatous optic neuropathy (PGON). Each sequence of fields was projected along seven VIM glaucoma axes. Linear regression (LR) slopes generated from projections onto each axis yielded a degree of confidence (DOC) that there was progression. At 95% specificity, progression cutoffs were established for POP, visual field index (VFI), and mean deviation (MD). Guided progression analysis (GPA) was also compared. Results. POP identified a statistically similar number of eyes (P > 0.05) as progressing compared with VFI, MD, and GPA in suspects (3.8%, 2.7%, 5.6%, and 2.9%, respectively), and more eyes than GPA (P = 0.01) in glaucoma (16.0%, 15.3%, 12.0%, and 7.3%, respectively), and more eyes than GPA (P = 0.05) in PGON eyes (26.3%, 23.7%, 27.6%, and 14.5%, respectively). Conclusions. POP, with its display of DOC of progression and its identification of progressing VF defect pattern, adds to the information available to the clinician for detecting VF progression.

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