title: Relevance vector machine and support vector machine classifier analysis of scanning laser polarimetry retinal nerve fiber layer measurements.
abstract: OBJECTIVE
  To classify healthy and glaucomatous eyes using relevance vector machine (RVM) and support vector machine (SVM) learning classifiers trained on retinal nerve fiber layer (RNFL) thickness measurements obtained by scanning laser polarimetry (SLP).
  
  
  METHODS
  Seventy-two eyes of 72 healthy control subjects (average age = 64.3 +/- 8.8 years, visual field mean deviation = -0.71 +/- 1.2 dB) and 92 eyes of 92 patients with glaucoma (average age = 66.9 +/- 8.9 years, visual field mean deviation = -5.32 +/- 4.0 dB) were imaged with SLP with variable corneal compensation (GDx VCC; Laser Diagnostic Technologies, San Diego, CA). RVM and SVM learning classifiers were trained and tested on SLP-determined RNFL thickness measurements from 14 standard parameters and 64 sectors (approximately 5.6 degrees each) obtained in the circumpapillary area under the instrument-defined measurement ellipse (total 78 parameters). Ten-fold cross-validation was used to train and test RVM and SVM classifiers on unique subsets of the full 164-eye data set and areas under the receiver operating characteristic (AUROC) curve for the classification of eyes in the test set were generated. AUROC curve results from RVM and SVM were compared to those for 14 SLP software-generated global and regional RNFL thickness parameters. Also reported was the AUROC curve for the GDx VCC software-generated nerve fiber indicator (NFI).
  
  
  RESULTS
  The AUROC curves for RVM and SVM were 0.90 and 0.91, respectively, and increased to 0.93 and 0.94 when the training sets were optimized with sequential forward and backward selection (resulting in reduced dimensional data sets). AUROC curves for optimized RVM and SVM were significantly larger than those for all individual SLP parameters. The AUROC curve for the NFI was 0.87.
  
  
  CONCLUSIONS
  Results from RVM and SVM trained on SLP RNFL thickness measurements are similar and provide accurate classification of glaucomatous and healthy eyes. RVM may be preferable to SVM, because it provides a Bayesian-derived probability of glaucoma as an output. These results suggest that these machine learning classifiers show good potential for glaucoma diagnosis.
authors.affiliation: Hamilton Glaucoma Center, University of California, San Diego, California, USA. cbowd@eyecenter.ucsd.edu
authors.forename: Christopher
authors.initials: C
authors.lastname: Bowd
authors.forename: Felipe A
authors.initials: FA
authors.lastname: Medeiros
authors.forename: Zuohua
authors.initials: Z
authors.lastname: Zhang
authors.forename: Linda M
authors.initials: LM
authors.lastname: Zangwill
authors.forename: Jiucang
authors.initials: J
authors.lastname: Hao
authors.forename: Te-Won
authors.initials: TW
authors.lastname: Lee
authors.forename: Terrence J
authors.initials: TJ
authors.lastname: Sejnowski
authors.forename: Robert N
authors.initials: RN
authors.lastname: Weinreb
authors.forename: Michael H
authors.initials: MH
authors.lastname: Goldbaum
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D019540:Area Under Curve; D003430:Cross-Sectional Studies; D003952:Diagnostic Imaging / Q000379:methods*; D003941:Diagnostic Techniques, Ophthalmological; D005901:Glaucoma / Q000145:classification / Q000175:diagnosis*; D006801:Humans; D007834:Lasers; D008875:Middle Aged; D009412:Nerve Fibers / Q000473:pathology*; D009901:Optic Nerve Diseases / Q000145:classification / Q000175:diagnosis*; D012372:ROC Curve; D012165:Retinal Ganglion Cells / Q000473:pathology*; D012680:Sensitivity and Specificity