Application of Shape-based Analysis Methods to OCT Retinal Nerve Fiber Layer Data in Glaucoma

Purpose(1) To evaluate the performance of shape-based analysis [wavelet-Fourier analysis (WFA) and fast Fourier analysis (FFA)] applied to retinal nerve fiber layer (RNFL) thickness values obtained from the optical coherence tomograph (OCT) to discriminate healthy and glaucomatous eyes. (2) To compare the performance of the shape-based metrics to that of the standard OCT output measures (Inferior Average and Average Thickness). MethodsRNFL values were obtained from 152 eyes of 152 individuals (83 healthy and 69 “mild”-stage perimetric glaucoma). WFA and FFA were performed on the RNFL values and linear discriminant functions for both were obtained using Fisher linear discriminant analysis. Performance was evaluated by calculating sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (ROC area). ResultsThe ROC area of the shape-based methods [0.94 (WFA) and 0.88 (FFA)] was greater than that of OCT metrics [0.81 (Inferior Average) and 0.74 (Average Thickness)]. Specifically, WFAs performance was significantly better than both the FFA (P=0.009) and the Inferior Average (P=0.001). Inferior average performed significantly better than Average Thickness (P=0.006). ConclusionsThe ability to differentiate glaucomatous from healthy eyes using stratus OCT measurements is improved by using these analysis methods that emphasize the shape of the RNFL thickness pattern.