title: Using unsupervised learning with independent component analysis to identify patterns of glaucomatous visual field defects.
abstract: OBJECTIVE
  Clustering by unsupervised learning with machine learning classifiers was shown to segment clusters of patterns in standard automated perimetry (SAP) for glaucoma in previous publications. In this study, unsupervised learning by independent component analysis decomposed SAP field patterns into axes, and the information represented by these axes was evaluated.
  
  
  METHODS
  SAP fields were used that were obtained with the Humphrey Visual Field Analyzer (Carl Zeiss Meditec, Dublin, CA) from 189 normal eyes and 156 eyes with glaucomatous optic neuropathy (GON) determined by masked review with stereoscopic optic disc photographs. The variational Bayesian independent component analysis mixture model (vB-ICA-mm) partitioned the SAP fields into the most informative number of clusters. Simultaneously, the model learned an optimal number of maximally independent axes for each cluster.
  
  
  RESULTS
  The most informative number of clusters in the SAP set was two. vB-ICA-mm placed 68.6% of the eyes with GON in a cluster labeled G and 98.4% of the eyes with normal optic discs in a cluster labeled N. Cluster G optimally contained six axes. Post hoc analysis of patterns generated at -1 SD and +2 SD from the cluster G mean on the six axes revealed defects similar to those identified by experts as indicative of glaucoma. SAP fields associated with an axis showed increasing severity, as they were located farther in the positive direction from the cluster G mean.
  
  
  CONCLUSIONS
  vB-ICA-mm represented the SAP fields with patterns that were meaningful for glaucoma experts. This process also captured severity in the patterns uncovered. These findings should validate vB-ICA-mm as a data-mining technique for new and unfamiliar complex tests.
authors.affiliation: Ophthalmic Informatics Laboratory and Hamilton Glaucoma Center, Department of Ophthalmology, University of California at San Diego, La Jolla, 92093, USA. mgoldbaum@ucsd.edu
authors.forename: Michael H
authors.initials: MH
authors.lastname: Goldbaum
authors.forename: Pamela A
authors.initials: PA
authors.lastname: Sample
authors.forename: Zuohua
authors.initials: Z
authors.lastname: Zhang
authors.forename: Kwokleung
authors.initials: K
authors.lastname: Chan
authors.forename: Jiucang
authors.initials: J
authors.lastname: Hao
authors.forename: Te-Won
authors.initials: TW
authors.lastname: Lee
authors.forename: Catherine
authors.initials: C
authors.lastname: Boden
authors.forename: Christopher
authors.initials: C
authors.lastname: Bowd
authors.forename: Rupert
authors.initials: R
authors.lastname: Bourne
authors.forename: Linda
authors.initials: L
authors.lastname: Zangwill
authors.forename: Terrence
authors.initials: T
authors.lastname: Sejnowski
authors.forename: David
authors.initials: D
authors.lastname: Spinak
authors.forename: Robert N
authors.initials: RN
authors.lastname: Weinreb
mesh_terms: D001185:Artificial Intelligence; D003941:Diagnostic Techniques, Ophthalmological; D005902:Glaucoma, Open-Angle / Q000175:diagnosis*; D006801:Humans; D007429:Intraocular Pressure; D008875:Middle Aged; D009901:Optic Nerve Diseases / Q000175:diagnosis*; D014786:Vision Disorders / Q000175:diagnosis*; D014794:Visual Fields