title: Unsupervised Gaussian Mixture-Model With Expectation Maximization for Detecting Glaucomatous Progression in Standard Automated Perimetry Visual Fields.
abstract: OBJECTIVE
  To validate Gaussian mixture-model with expectation maximization (GEM) and variational Bayesian independent component analysis mixture-models (VIM) for detecting glaucomatous progression along visual field (VF) defect patterns (GEM-progression of patterns (POP) and VIM-POP). To compare GEM-POP and VIM-POP with other methods.
  
  
  METHODS
  GEM and VIM models separated cross-sectional abnormal VFs from 859 eyes and normal VFs from 1117 eyes into abnormal and normal clusters. Clusters were decomposed into independent axes. The confidence limit (CL) of stability was established for each axis with a set of 84 stable eyes. Sensitivity for detecting progression was assessed in a sample of 83 eyes with known progressive glaucomatous optic neuropathy (PGON). Eyes were classified as progressed if any defect pattern progressed beyond the CL of stability. Performance of GEM-POP and VIM-POP was compared to point-wise linear regression (PLR), permutation analysis of PLR (PoPLR), and linear regression (LR) of mean deviation (MD), and visual field index (VFI).
  
  
  RESULTS
  Sensitivity and specificity for detecting glaucomatous VFs were 89.9% and 93.8%, respectively, for GEM and 93.0% and 97.0%, respectively, for VIM. Receiver operating characteristic (ROC) curve areas for classifying progressed eyes were 0.82 for VIM-POP, 0.86 for GEM-POP, 0.81 for PoPLR, 0.69 for LR of MD, and 0.76 for LR of VFI.
  
  
  CONCLUSIONS
  GEM-POP was significantly more sensitive to PGON than PoPLR and linear regression of MD and VFI in our sample, while providing localized progression information.
  
  
  CONCLUSIONS
  Detection of glaucomatous progression can be improved by assessing longitudinal changes in localized patterns of glaucomatous defect identified by unsupervised machine learning.
authors.affiliation: Hamilton Glaucoma Center and the Department of Ophthalmology University of California San Diego, La Jolla, CA, USA.
authors.forename: Siamak
authors.initials: S
authors.lastname: Yousefi
authors.affiliation: Department of Electrical and Computer Engineering; Department of Biomedical Engineering, University of Memphis, Memphis, TN, USA.
authors.forename: Madhusudhanan
authors.initials: M
authors.lastname: Balasubramanian
authors.affiliation: Hamilton Glaucoma Center and the Department of Ophthalmology University of California San Diego, La Jolla, CA, USA.
authors.forename: Michael H
authors.initials: MH
authors.lastname: Goldbaum
authors.affiliation: Hamilton Glaucoma Center and the Department of Ophthalmology University of California San Diego, La Jolla, CA, USA.
authors.forename: Felipe A
authors.initials: FA
authors.lastname: Medeiros
authors.affiliation: Hamilton Glaucoma Center and the Department of Ophthalmology University of California San Diego, La Jolla, CA, USA.
authors.forename: Linda M
authors.initials: LM
authors.lastname: Zangwill
authors.affiliation: Hamilton Glaucoma Center and the Department of Ophthalmology University of California San Diego, La Jolla, CA, USA.
authors.forename: Robert N
authors.initials: RN
authors.lastname: Weinreb
authors.affiliation: Department of Ophthalmology, New York University, New York, NY, USA.
authors.forename: Jeffrey M
authors.initials: JM
authors.lastname: Liebmann
authors.affiliation: Department of Ophthalmology, University of Alabama, Birmingham, AL, USA.
authors.forename: Christopher A
authors.initials: CA
authors.lastname: Girkin
authors.affiliation: Hamilton Glaucoma Center and the Department of Ophthalmology University of California San Diego, La Jolla, CA, USA.
authors.forename: Christopher
authors.initials: C
authors.lastname: Bowd
keywords: Gaussian mixture-model; glaucoma; progression detection; standard automated perimetry; unsupervised machine learning; visual field