Discriminating between Glaucoma and Normal Eyes Using Optical Coherence Tomography and the 'Random Forests' Classifier

Purpose: To diagnose glaucoma based on spectral domain optical coherence tomography (SD-OCT) measurements using the 'Random Forests' method. Methods: SD-OCT was conducted in 126 eyes of 126 open angle glaucoma (OAG) patients and 84 eyes of 84 normal subjects. The Random Forests method was then applied to discriminate between glaucoma and normal eyes using 151 OCT parameters including thickness measurements of circumpapillary retinal nerve fiber layer (cpRNFL), the macular RNFL (mRNFL) and the ganglion cell layer-inner plexiform layer combined (GCIPL). The area under the receiver operating characteristic curve (AROC) was calculated using the Random Forests method adopting leave-one-out cross validation. For comparison, AROCs were calculated based on each one of the 151 OCT parameters. Results: The AROC obtained with the Random Forests method was 98.5% [95% Confidence interval (CI): 97.1-99.9%], which was significantly larger than the AROCs derived from any single OCT parameter (maxima were: 92.8 [CI: 89.4-96.2] %, 94.3 [CI: 91.1-97.6] % and 91.8 [CI: 88.2-95.4] % for cpRNFL-, mRNFL- and GCIPL-related parameters, respectively; P < 0.05, DeLong's method with Holm's correction for multiple comparisons). The partial AROC above specificity of 80%, for the Random Forests method was equal to 18.5 [CI: 16.8-19.6] %, which was also significantly larger than the AROCs of any single OCT parameter (P < 0.05, Bootstrap method with Holm's correction for multiple comparisons). Conclusions: The Random Forests method, analyzing multiple SD-OCT parameters concurrently, significantly improves the diagnosis of glaucoma compared with using any single SD-OCT measurement.