Evaluation of the Diagnostic Capability of Spectralis SD-OCT 8 × 8 Posterior Pole Software with the Grid Tilted at 7 Degrees and Horizontalized in Glaucoma.

Background: The goal was to evaluate the diagnostic capability of different parameters obtained with the posterior pole (PP) software in Spectralis SD-OCT with the 8 × 8 grid tilted at 7° and horizontalized in glaucomatous eyes. Methods: A total of 299 eyes were included, comprising 136 healthy eyes and 163 with primary open-angle glaucoma (POAG). The following segmentations were evaluated: complete retina, retinal nerve fiber layer (RNFL), ganglion cell layer (GCL), GCL and inner plexiform layer (GCLIPL), ganglion cell complex (GCC), outer plexiform layer and outer nuclear layer (OPLONL), inner retinal layer (IRL), and outer retinal layer (ORL). Different patterns of macular damage were represented using heatmaps for each studied layer, where the areas under the curve (AUROC) values and a retinal thickness cutoff point were defined to discriminate POAG patients. Results: There was not any difference in the diagnostic capability for detecting glaucoma between the grid tilted at 7° and horizontalized. The macular segmentations that offer the highest diagnostic ability in glaucoma discrimination were, in the following order, RNFL (AUROC = 0.796), GCC (AUROC = 0.785), GCL (AUROC = 0.784), GCLIPL (AUROC = 0.770), IRL (AUROC = 0.755), and the complete retina (AUROC = 0.752). In contrast, ORL and OPLONL do not appear to be helpful for discriminating POAG. Conclusions: Some results of PP software may be useful for discriminating POAG.

Normative Database of the Superior-Inferior Thickness Asymmetry for All Inner and Outer Macular Layers of Adults for the Posterior Pole Algorithm of the Spectralis SD-OCT.

BACKGROUND: This study aims to establish a reference for the superior-inferior hemisphere asymmetry in thickness values for all macular layers for the posterior pole algorithm (PPA) available for the Spectralis SD-OCT device. METHODS: We examined 300 eyes of 300 healthy Caucasian volunteers aged 18-84 years using the PPA, composed of a grid of 64 (8 × 8) cells, to analyze the thickness asymmetries of the following automatically segmented macular layers: retinal nerve fiber layer (RNFL); ganglion cell layer (GCL); inner plexiform layer (IPL); inner nuclear layer (INL); outer plexiform layer (OPL); outer nuclear layer (ONL); retinal pigment epithelium (RPE); inner retina; outer retina; complete retina. Mean ± standard deviation and the 2.5th and 97.5th percentiles of the thickness asymmetry values were obtained for all the corresponding cells. RESULTS: All the macular layers had significant superior-inferior thickness asymmetries. GCL, IPL, INL, ONL and RPE showed significantly greater thicknesses in the superior than the inferior hemisphere, whereas RNFL and OPL were thicker in the inferior hemisphere. The largest differences between hemispheres were for RNFL and ONL. CONCLUSIONS: This is the first normative database of macular thickness asymmetries for the PPA and should be considered to distinguish normal from pathological values when interpreting superior-inferior macular asymmetries.

PAPILA: Dataset with fundus images and clinical data of both eyes of the same patient for glaucoma assessment.

Glaucoma is one of the ophthalmological diseases that frequently causes loss of vision in today's society. Previous studies assess which anatomical parameters of the optic nerve can be predictive of glaucomatous damage, but to date there is no test that by itself has sufficient sensitivity and specificity to diagnose this disease. This work provides a public dataset with medical data and fundus images of both eyes of the same patient. Segmentations of the cup and optic disc, as well as the labeling of the patients based on the evaluation of clinical data are also provided. The dataset has been tested with a neural network to classify healthy and glaucoma patients. Specifically, the ResNet-50 has been used as the basis to classify patients using information from each eye independently as well as using the joint information from both eyes of each patient. Results provide the baseline metrics, with the aim of promoting research in the early detection of glaucoma based on the joint analysis of both eyes of the same patient.

Normative Database for All Retinal Layer Thicknesses Using SD-OCT Posterior Pole Algorithm and the Effects of Age, Gender and Axial Lenght.

Our aim was to provide, for the first time, reference thickness values for the SD-OCT posterior pole algorithm (PPA) available for Spectralis OCT device (Heidelberg Engineering, Heidelberg, Germany) and to analyze the correlations with age, gender and axial length. We recruited 300 eyes of 300 healthy Caucasian subjects between 18 and 84 years. By PPA, composed of 64 (8 × 8) cells, we analyzed the thickness of the following macular layers: retinal nerve fiber layer (RNFL), ganglion cell layer (GCL), inner plexiform layer (IPL), inner nuclear layer (INL), outer plexiform layer (OPL), outer nuclear layer (ONL), retinal pigment epithelium (RPE), inner retina, outer retina and full retina. Mean ± SD, 1st, 5th, 95th percentiles were obtained for each cell at all macular layers. Significant negative correlations were found between age and thickness for most macular layers. The mean thickness of most macular layers was thicker for men than women, except for RNFL, OPL and RPE, with no gender differences. GCL, IPL and INL thicknesses positively correlated with axial length in central cells, and negatively in the cells near the optic disk. The mean RNFL thickness was positively associated with axial length. This is the first normative database for PPA. Age, gender and axial length should be taken into account when interpreting PPA results.

Glaucomatous Maculopathy: Thickness Differences on Inner and Outer Macular Layers between Ocular Hypertension and Early Primary Open-Angle Glaucoma Using 8 × 8 Posterior Pole Algorithm of SD-OCT.

The purpose of this study was to compare the thickness of all inner and outer macular layers between ocular hypertension (OHT) and early primary open-angle glaucoma (POAG) using spectral domain optical coherence tomography (SD-OCT) 8 × 8 posterior pole algorithm (8 × 8 PPA). Fifty-seven eyes of 57 OHT individuals and fifty-seven eyes of 57 early POAG patients were included. The thickness of macular retinal nerve fiber layer (mRNFL), ganglion cell layer (GCL), inner plexiform layer (IPL), inner nuclear layer (INL), outer plexiform and nuclear layer, photoreceptor layer (PRL) and retinal pigment epithelium were obtained in 64 cells for each macular layer and mean thickness of superior and inferior hemispheres was also calculated. Thinning of superior and inferior hemisphere mean thickness in mRNFL, GCL and IPL and thickening of superior and inferior hemisphere mean thickness in PRL and inferior hemisphere in INL were found in early GPAA group. Otherwise, heatmaps representing cell-to-cell comparisons showed thinning patterns in inner retinal layers (except for INL) and thickening patterns in outer retinal layers in GPAA group. We found that 8 × 8 PPA not only allows the detection of significant thinning patterns in inner retinal layers, but also thickening patterns in outer retinal layers when comparing early POAG eyes to OHT eyes.