title: Development and comparison of automated classifiers for glaucoma diagnosis using Stratus optical coherence tomography.
abstract: OBJECTIVE
  To develop and compare the ability of several automated classifiers to differentiate between normal and glaucomatous eyes based on the quantitative assessment of summary data reports from Stratus optical coherence tomography (OCT; Carl Zeiss Meditec Inc., Dublin, CA) in a Chinese population in Taiwan.
  
  
  METHODS
  One randomly selected eye from each of 89 patients with glaucoma and each of 100 age- and sex-matched normal individuals were included in the study. Measurements of glaucoma variables (retinal nerve fiber layer thickness and optic nerve head analysis results) were obtained by Stratus OCT. With the Stratus OCT parameters used as input, receiver operative characteristic (ROC) curves were generated by three methods, to classify eyes as either glaucomatous or normal: linear discriminant analysis (LDA), Mahalanobis distance (MD), and artificial neural network (ANN). The area under the ROC curve was optimized by principal component analysis (PCA). Classification accuracy was determined by cross validation.
  
  
  RESULTS
  The average visual field mean deviation was -0.7 +/- 0.6 dB in the normal group and -2.7 +/- 1.9 dB in the glaucoma group. The areas under the ROC curves were 0.824 (LDA), 0.849 (MD), 0.821 (ANN), 0.915 (LDA with PCA), 0.991 (MD with PCA), and 0.874 (ANN with PCA).
  
  
  CONCLUSIONS
  With Stratus OCT parameters used as input, automated classifiers show promise for discriminating between glaucomatous and normal eyes. MD measured from multivariate data can predict the severity of glaucoma through the construction of a measurement space. After PCA, implementation results show that the Mahalanobis space created by MD surpasses LDA and ANN in diagnosing glaucoma.
authors.affiliation: Department of Industrial Engineering and Management, National Chin-Yi Institute of Technology, Taichung County, Taiwan.
authors.forename: Mei-Ling
authors.initials: ML
authors.lastname: Huang
authors.forename: Hsin-Yi
authors.initials: HY
authors.lastname: Chen
mesh_terms: D000328:Adult; D044466:Asian People / Q000208:ethnology; D003430:Cross-Sectional Studies; D003941:Diagnostic Techniques, Ophthalmological; D005260:Female; D005902:Glaucoma, Open-Angle / Q000145:classification* / Q000175:diagnosis* / Q000208:ethnology; D006801:Humans; D007429:Intraocular Pressure; D008297:Male; D008875:Middle Aged; D009412:Nerve Fibers / Q000473:pathology*; D016571:Neural Networks, Computer; D009898:Optic Disk / Q000473:pathology; D009901:Optic Nerve Diseases / Q000175:diagnosis*; D012372:ROC Curve; D012165:Retinal Ganglion Cells / Q000473:pathology*; D012680:Sensitivity and Specificity; D013624:Taiwan / Q000453:epidemiology; D041623:Tomography, Optical Coherence / Q000379:methods*; D014794:Visual Fields