title: Predicting glaucomatous progression in glaucoma suspect eyes using relevance vector machine classifiers for combined structural and functional measurements.
abstract: OBJECTIVE
  The goal of this study was to determine if glaucomatous progression in suspect eyes can be predicted from baseline confocal scanning laser ophthalmoscope (CSLO) and standard automated perimetry (SAP) measurements analyzed with relevance vector machine (RVM) classifiers.
  
  
  METHODS
  Two hundred sixty-four eyes of 193 participants were included. All eyes had normal SAP results at baseline with five or more SAP tests over time. Eyes were labeled progressed (n = 47) or stable (n = 217) during follow-up based on SAP Guided Progression Analysis or serial stereophotograph assessment. Baseline CSLO-measured topographic parameters (n = 117) and baseline total deviation values from the 24-2 SAP test-grid (n = 52) were selected from each eye. Ten-fold cross-validation was used to train and test RVMs using the CSLO and SAP features. Receiver operating characteristic (ROC) curve areas were calculated using full and optimized feature sets. ROC curve results from RVM analyses of CSLO, SAP, and CSLO and SAP combined were compared to CSLO and SAP global indices (Glaucoma Probability Score, mean deviation and pattern standard deviation).
  
  
  RESULTS
  The areas under the ROC curves (AUROCs) for RVMs trained on optimized feature sets of CSLO parameters, SAP parameters, and CSLO and SAP parameters combined were 0.640, 0.762, and 0.805, respectively. AUROCs for CSLO Glaucoma Probability Score, SAP mean deviation (MD), and SAP pattern standard deviation (PSD) were 0.517, 0.513, and 0.620, respectively. No CSLO or SAP global indices discriminated between baseline measurements from progressed and stable eyes better than chance.
  
  
  CONCLUSIONS
  In our sample, RVM analyses of baseline CSLO and SAP measurements could identify eyes that showed future glaucomatous progression with a higher accuracy than the CSLO and SAP global indices. (ClinicalTrials.gov numbers, NCT00221897, NCT00221923.).
authors.affiliation: Hamilton Glaucoma Center, Department of Ophthalmology, University of California, San Diego, La Jolla, CA 92037-0946, USA. cbowd@glaucoma.ucsd.edu
authors.forename: Christopher
authors.initials: C
authors.lastname: Bowd
authors.forename: Intae
authors.initials: I
authors.lastname: Lee
authors.forename: Michael H
authors.initials: MH
authors.lastname: Goldbaum
authors.forename: Madhusudhanan
authors.initials: M
authors.lastname: Balasubramanian
authors.forename: Felipe A
authors.initials: FA
authors.lastname: Medeiros
authors.forename: Linda M
authors.initials: LM
authors.lastname: Zangwill
authors.forename: Christopher A
authors.initials: CA
authors.lastname: Girkin
authors.forename: Jeffrey M
authors.initials: JM
authors.lastname: Liebmann
authors.forename: Robert N
authors.initials: RN
authors.lastname: Weinreb
mesh_terms: D018450:Disease Progression; D005260:Female; D005901:Glaucoma / Q000175:diagnosis*; D006801:Humans; D007429:Intraocular Pressure / Q000502:physiology; D008297:Male; D018613:Microscopy, Confocal / Q000379:methods; D008875:Middle Aged; D009887:Ophthalmoscopy / Q000379:methods; D011379:Prognosis; D012680:Sensitivity and Specificity; D060388:Support Vector Machine; D013997:Time Factors; D058609:Visual Field Tests