title: Unsupervised learning with independent component analysis can identify patterns of glaucomatous visual field defects.
abstract: OBJECTIVE
  We previously reported the use of clustering by unsupervised learning with machine learning classifiers to segment clusters of patterns in standard automated perimetry (SAP) for glaucoma. In this study, the process of 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 obtained with the Humphrey Visual Field Analyzer on 189 normal eyes and 156 eyes with glaucomatous optic neuropathy (GON) determined by masked review with stereoscopic optic disc photos. The variational Bayesian independent component analysis mixture model (vB-ICA-mm) partitioned the SAP fields into the most informative number of clusters. Simultaneously, it learned an optimal number of maximally independent axes for each cluster.
  
  
  RESULTS
  The most informative number of clusters was two. vB-ICA-mm placed 68.6% of the SAP fields from eyes with GON in a cluster labeled G and 98.4% of the fields from 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: Department of Ophthalmology, University of California, San Diego, La Jolla, California, USA.
authors.forename: Michael Henry
authors.initials: MH
authors.lastname: Goldbaum
mesh_terms: D001185:Artificial Intelligence; D001499:Bayes Theorem; D003936:Diagnosis, Computer-Assisted; D005901:Glaucoma / Q000150:complications* / Q000175:diagnosis*; D006801:Humans; D007858:Learning; D009901:Optic Nerve Diseases / Q000150:complications; D012720:Severity of Illness Index; D014786:Vision Disorders / Q000175:diagnosis* / Q000209:etiology*; D058609:Visual Field Tests; D014794:Visual Fields