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Progression of Patterns (POP): A Machine Classifier Algorithm to Identify Glaucoma Progression in Visual Fields

Year: 2012
Type: article
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.
DOI: 10.1167/iovs.11-8363
Authors: Michael H. Goldbaum, Intae Lee, Giljin Jang, Madhusudhanan Balasubramanian, Pamela A. Sample, Robert N. Weinreb, Jeffrey M. Liebmann, Christopher A. Girkin, Douglas R. Anderson, Linda M. Zangwill, Marie-Josee Fredette, Tzyy-Ping Jung, Felipe A. Medeiros, Christopher Bowd
Institutions: University of California San Diego, New York Eye and Ear Infirmary, University of Alabama, University of Alabama at Birmingham, University of Miami, Université Laval
Language: English
Subfield: Ophthalmology
Field: Medicine
Open Access status: bronze
Funders: EyeSight Foundation of Alabama, Glaucoma Research Foundation, Pfizer, National Institutes of Health, Allergan, National Eye Institute
Awards: Quan11tification of Glaucomatous Optic Neuropathy, G1228582304, Diagnostic Innovations in Glaucoma Study: Visual Function, G1424480964, G1485955216, G1585253682, G1610538956, Medical Advice from Glaucoma Informatics (MAGI), G3662681498, Predicting and Detecting Glaucomatous Progression Using Pattern Recognition, G4852347705, Medical Advice from Glaucoma Informatics (MAGI), African Descent and Glaucoma Evaluation (ADAGES) II: Glaucoma Progression, G5228809850, Diagnostic Innovations in Glaucoma Study (DIGS): Functional Impairment, Diagnostic Innovations in Glaucoma: Structural Assessment, G6217192593, G9000343554, NEI Center Core Grant for Vision Research
OpenAlex ID: W2033972793
FWCI: 2.102
Cites: 24
Cited by: 45
Related to: 10