Linear discriminant analysis and artificial neural network for glaucoma diagnosis using scanning laser polarimetry-variable cornea compensation measurements in Taiwan Chinese population

Abstract

Background: To determine whether linear discriminant analysis (LDA) and artificial neural network (ANN) can improve the differentiation between glaucomatous and normal eyes in a Taiwan Chinese population, based on the retinal nerve fiber layer thickness measurement data from scanning laser polarimetry-variable corneal compensation (GDx VCC).

Methods: This study comprised 79 glaucoma (visual field defect, mean deviation: -5.60 +/- 4.23 dB) and 86 healthy subjects (visual field defect, mean deviation: -1.44 +/- 1.72 dB). Each patient received complete ophthalmological evaluation, standard automated perimetry (SAP), and GDx VCC exam. One eye per subject was considered for further analysis. The area under the receiver operating characteristics (AROC) curve, sensitivity, specificity and the best cut-off value for each parameter were calculated. The diagnostic performance of artificial neural network (ANN) and linear discriminant analysis (LDA) for glaucoma detection using GDx VCC measurements will be compared in this study.

Results: The individual parameter with the best AROC curve for differentiating between normal and glaucomatous eye was nerve fiber indicator (NFI, 0.932). The highest AROCs for the LDA and ANN methods were 0.950 and 0.970 respectively.

Conclusion: NFI, ANN and LDF method demonstrated equal diagnostic power in glaucoma detection in a Taiwan Chinese population.