Wavelet Features of the Thickness Map of Retinal Ganglion Cell-Inner Plexiform Layer Best Discriminate Prior Optic Neuritis in Patients With Multiple Sclerosis

The goal of this study was to analyze wavelet features of topographic thickness maps of the retinal ganglion cell-inner plexiform layer (GCIPL) and evaluate their discrimination ability in patients with multiple sclerosis (MS) and a history of optic neuritis (ON). Twenty-nine patients with relapsing-remitting MS and a history of ON were recruited together with 63 age- and sex-matched controls (HC). There were 33 eyes with a history of ON (MSON), 25 non-ON fellow eyes (MSFE) and 63 HC eyes. Ultrahigh-resolution optical coherence tomography was used to image the macula of each participant, and the volumetric dataset was segmented to yield a topographic thickness map of the GCIPL. The thickness map was analyzed using discrete wavelet transform to extract useful features, which were further analyzed with three machine learning methods, including logistic regression (LR), logistic regression regularized with the elastic net penalty (LR-EN), and support vector machine (SVM), for classification between groups. LR-EN with nested leave-one-out cross-validation resulted in 94% (83%) sensitivity and 100% (97%) specificity in discriminating MSON (MSFE) eyes from HC eyes in the training data and 88% (64%) sensitivity and 94% (94%) specificity in discriminating MSON (MSFE) eyes from HC eyes in the validation data, which were similar to or better than those resulting from LR and SVM. LR-EN improved the sensitivity and specificity of discriminating prior ON and MSFE eyes in patients with MS from HC eyes compared with traditional thickness analysis. It may be promising in facilitating the diagnosis of subclinical ON in patients with MS.