For this novel software, we first acquired high density 6 × 6 mm macular volume scans and then used our custom-designed software to analyze the total retina and GCC retina for both macular thickness and volume parameters for six different-sized annular regions. The first aim was to develop customized glaucoma software for macular analysis in the clinic to see if a macular parameter could be developed that is better than the traditional RNFL thickness parameter. 24 – 38īecause a review of the currently available macular software for glaucoma patients suggests that macular analysis is no better than the most commonly used RNFL thickness parameter, 24 – 38 this study had two specific aims. 24, 25 For all of these 3 commonly used SD-OCT platforms, a literature review generally suggests that the current commercially available SD-OCT macular software has the same or worse diagnostic potential as the traditional RNFL thickness parameter. Some studies have even used the commercially available ETDRS (Early Treatment Diabetic Retinopathy Study) circles, which were designed for assessing diabetic disease and not glaucoma, to assess macular thickness in glaucoma patients. For the Spectralis HRA OCT platform (Heidelberg Engineering, Heidelberg, Germany), software for posterior pole asymmetry analysis (PPAA) is available to evaluate total retinal thickness in the macular region in a 30° × 25° scan region, which was divided into an 8 × 8 grid. The Cirrus platform (software versions 6.0 or higher Carl Zeiss Meditec, Inc., Dublin, CA, USA) analyzes the macular region with the ganglion cell analysis, which measures the combined thickness of two layers (i.e., the GCL and the IPL) or GCIPL. 21 – 23 The RTVue SD-OCT platform (Optovue, Inc., Fremont, CA, USA) calls these 3 layers the ganglion cell complex (GCC). 20 Spectral domain (SD) OCT has enabled the segmentation of the three innermost layers of the macular retina: the retinal nerve fiber layer, the ganglion cell layer (GCL), and the inner plexiform layer (IPL). 9 – 19 Approximately 50% of the over 1 million retinal ganglion cells (RGCs) contained in the human retina are concentrated within 4.5 mm of the fovea, and the macula is the only area where the ganglion cell layer is more than one cell layer thick (up to seven layers). In addition to the traditional RNFL thickness parameter, the macula has recently become a target for structural glaucoma analysis. Manual correction of artifacts with data interpolation is unnecessary in the clinical setting. The diagnostic performance of best macular parameters (GCC-volume-34 and GCC-thickness-34) were similar to or better than 2D RNFL thickness. Correction of artifacts did not significantly change the AUROC of macular parameters ( P values between 0.8452 and 1.0000). The AUROC for RNFL thickness and GCC-volume-34 were statistically similar for all regions (global: RNFL thickness 0.956, GCC-volume-34 0.939, P value = 0.3827), except for the temporal GCC-volume-34, which was significantly better than temporal RNFL thickness ( P value = 0.0067). The 3D macular parameter with the best diagnostic performance was GCC-volume-34, with an inner diameter of 3 mm and an outer of 4 mm. The areas under the receiver operating characteristic curves (AUROC) were calculated for all the parameters. All macular parameters were calculated with and without correction and interpolation of frames with artifacts. Four parameters were calculated for six different-sized annuli: total macular thickness (M-thickness), total macular volume (M-volume), ganglion cell complex (GCC) thickness, and GCC volume of the innermost 3 macular layers (retinal nerve fiber layer + ganglion cell layer + inner plexiform layer). To determine if manual correction and interpolation of B-scans improve the ability of 3D macular parameters to diagnose glaucoma.Ī total of 101 open angle glaucoma patients (29 with early glaucoma) and 57 healthy subjects had peripapillary 2D RNFL thickness and 3D macular volume scans. To compare the diagnostic capability of three-dimensional (3D) macular parameters against traditional two-dimensional (2D) retinal nerve fiber layer (RNFL) thickness using spectral domain optical coherence tomography.
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