Classification Algorithms Enhance the Discrimination of Glaucoma from Normal Eyes Using High-Definition Optical Coherence Tomography

Abstract

To evaluate the diagnostic performance of classification algorithms based on Linear Discriminant Analysis (LDA) and Classification And Regression Tree (CART) methods, compared with optic nerve head (ONH) and retinal nerve fiber layer (RNFL) parameters measured by high-definition optical coherence tomography (Cirrus HD-OCT) for discriminating glaucoma subjects. Consecutive glaucoma subjects (Training data = 184; Validation data = 102) were recruited from an eye center and normal subjects (n = 508) from an ongoing Singaporean Chinese population-based study. ONH and RNFL parameters were measured using a 200 × 200 scan protocol. LDA and CART were computed and areas under the receiver operating characteristic curve (AUC) compared. Average RNFL thickness (AUC 0.92, 95% confidence interval [CI] 0.91, 0.93), inferior RNFL thickness (AUC 0.92, 95% CI 0.91, 0.93), vertical cup-disc ratio (AUC 0.91, 95% CI 0.90, 0.92) and rim area/disc area ratio (AUC 0.90, 95% CI 0.86, 0.93) discriminated glaucoma better than other parameters (P ≤ 0.033). LDA (AUC 0.96, 95% CI 0.95, 0.96) and CART (0.98, 95% CI 0.98, 0.99) outperformed all parameters for diagnostic accuracy (P ≤ 0.005). Misclassification rates in LDA (8%) and CART (5.6%) were found to be low. The AUC of LDA for the validation data was 0.98 (0.95, 0.99) and CART was 0.99 (0.99, 0.994). CART discriminated mild glaucoma from normal better than LDA (AUC 0.94 vs. 0.99, P < 0.0001). Classification algorithms based on LDA and CART can be used in HD-OCT analysis for glaucoma discrimination. The CART method was found to be superior to individual ONH and RNFL parameters for early glaucoma discrimination.