Retinal Nerve Fiber Layer Thickness Maps Research Articles

Multimodal Deep Learning Classifier for Primary Open-angle Glaucoma Diagnosis Using Wide-field Optic Nerve Head Cube Scans in Eyes With and Without High Myopia.

An OCT-based multimodal deep learning classification model including texture information is introduced that outperforms single-modal models and multimodal models without texture information for glaucoma diagnosis in eyes with and without high myopia. To evaluate the diagnostic accuracy of a multimodal deep learning (DL) classifier using wide optical coherence tomography (OCT) optic nerve head cube scans in eyes with and without axial high myopia. 371 primary open-angle glaucoma (POAG) eyes and 86 healthy eyes, all without axial high myopia (axial length AL≤26mm) and 92 POAG eyes and 44 healthy eyes, all with axial high myopia (AL>26mm) were included. The multimodal DL classifier combined features of 3 individual VGG-16 models: 1) texture-based en face image, 2) retinal nerve fiber layer (RNFL) thickness map image and 3) confocal scanning laser ophthalmoscope (cSLO) image. Age, AL, and disc area adjusted area under the receiver operating curves (AUROC) were used to compare model accuracy. Adjusted AUROC for the multimodal DL model was 0.91 (95% CI = 0.87, 0.95). This value was significantly higher than the values of individual models (0.83 [0.79, 0.86] for texture based en face image; 0.84 [0.81, 0.87] for RNFL thickness map; and 0.68 [0.61, 0.74] for cSLO image; all P≤0.05). Using only highly myopic eyes, the multimodal DL model showed significantly higher diagnostic accuracy (0.89 [0.86 , 0.92]) compared to texture en face image (0.83 [0.78 , 0.85]), RNFL (0.85 [0.81 , 0.86]) and cSLO image models (0.69 [0.63 , 0.76]) (all P≤0.05). Combining OCT-based RNFL thickness maps with texture based en face images showed better ability to discriminate between healthy and POAG than thickness maps alone, particularly in high axial myopic eyes.

An Artificial Intelligence Approach to Assess Spatial Patterns of Retinal Nerve Fiber Layer Thickness Maps in Glaucoma.

PurposeThe purpose of this study was to classify the spatial patterns of retinal nerve fiber layer thickness (RNFLT) and assess their associations with visual field (VF) loss in glaucoma.MethodsWe used paired reliable 24-2 VFs and optical coherence tomography scans of 691 eyes from 691 patients. The RNFLT maps were used to determine the RNFLT patterns (RPs) by non-negative matrix factorization (NMF). The RPs were correlated with mean deviation (MD), spherical equivalent (SE), and major blood vessel locations. The RPs were further used to predict the 52 total deviation (TD) values by linear regression compared with models using 24 15-degree sectors. Last, we associated the RPs with average TDs of the central upper two locations (C2-TD). Stepwise regression was applied to remove redundant features.ResultsNMF highlighted 16 distinct RPs. Twelve RPs had arcuate-like informative zones (iZones): six with superior iZones, five with inferior iZones, and one RP with a bi-hemifield iZone, and four with non-arcuate-like temporal or nasal iZones. Twelve, nine, nine, and nine RPs were significantly (P < 0.05) correlated to MD, SE, and superior and inferior artery locations, respectively. Using RPs significantly (P < 0.05) improved the prediction of 52 TDs compared with using 24 15-degree sectors. Using RPs significantly (P < 0.001) improved the C2-TD prediction related to thinning in the inferior vulnerability zone compared with using the 24 sectoral RNFLTs.ConclusionsUsing RPs improved the VF prediction compared with using sectoral RNFLTs.Translational RelevanceThe RPs characterizing both pathological and anatomical variations can potentially assist clinicians better assess RNFLT loss.

Development and Validation of a Deep Learning System for Diagnosing Glaucoma Using Optical Coherence Tomography.

This study aimed to develop and validate a deep learning system for diagnosing glaucoma using optical coherence tomography (OCT). A training set of 1822 eyes (332 control, 1490 glaucoma) with 7288 OCT images, an internal validation set of 425 eyes (104 control, 321 glaucoma) with 1700 images, and an external validation set of 355 eyes (108 control, 247 glaucoma) with 1420 images were included. Deviation and thickness maps of retinal nerve fiber layer (RNFL) and ganglion cell–inner plexiform layer (GCIPL) analyses were used to develop the deep learning system for glaucoma diagnosis based on the visual geometry group deep convolutional neural network (VGG-19) model. The diagnostic abilities of deep learning models using different OCT maps were evaluated, and the best model was compared with the diagnostic results produced by two glaucoma specialists. The glaucoma-diagnostic ability was highest when the deep learning system used the RNFL thickness map alone (area under the receiver operating characteristic curve (AUROC) 0.987), followed by the RNFL deviation map (AUROC 0.974), the GCIPL thickness map (AUROC 0.966), and the GCIPL deviation map (AUROC 0.903). Among combination sets, use of the RNFL and GCIPL deviation map showed the highest diagnostic ability, showing similar results when tested via an external validation dataset. The inclusion of the axial length did not significantly affect the diagnostic performance of the deep learning system. The location of glaucomatous damage showed generally high level of agreement between the heatmap and the diagnosis of glaucoma specialists, with 90.0% agreement when using the RNFL thickness map and 88.0% when using the GCIPL thickness map. In conclusion, our deep learning system showed high glaucoma-diagnostic abilities using OCT thickness and deviation maps. It also showed detection patterns similar to those of glaucoma specialists, showing promising results for future clinical application as an interpretable computer-aided diagnosis.

Diagnostic Accuracy of Wide-Field Map from Swept-Source Optical Coherence Tomography for Primary Open-Angle Glaucoma in Myopic Eyes

To compare the accuracy of glaucomatous defects and diagnostic power for primary open-angle glaucoma (POAG) between swept-source optical coherence tomography (SS-OCT) and spectral-domain OCT (SD-OCT) in myopic eyes. Prospective, case-control study. A total of 150 myopic POAG eyes and 100 healthy myopic eyes underwent SD-OCT and SS-OCT in random order on the same day. The locations of glaucomatous defects on SD-OCT thickness and deviation maps and SS-OCT wide-field thickness (thickness surfaces) and SuperPixel maps were rated, and the maps' accuracies were compared. The area under receiver operating characteristic (AUROC) of the peripapillary retinal nerve fiber layer (RNFL) and the macular parameters (GCL++: equivalent to ganglion cell-inner plexiform layer [GCIPL]+ RNFL; GCL+: equivalent to GCIPL) from each of the devices for myopic POAG were calculated and compared. The wide-field RNFL thickness (thickness surface) map showed the best accuracy for glaucomatous defects in the inferotemporal (96.4%) and superotemporal (92.4%) regions. The RNFL/GCL++/GCL+ wide-field thickness (thickness surface) map showed better accuracy for glaucomatous defects in both the superotemporal and inferotemporal regions than with the SD-OCT thickness map (all P values <.05). The average GCL++ (87.6%) and GCL+ (87.5%) thicknesses showed significantly greater AUROC for myopic POAG than GCIPL thickness from SD-OCT (83.8%, all P values <.05). In myopic eyes, the SS-OCT wide-field map exhibited better accuracy for glaucomatous defect and greater diagnostic power for POAG than SD-OCT outcomes. This result might have been due to SS-OCT's wider scan and measurement area.

Diagnosing Glaucoma With Spectral-Domain Optical Coherence Tomography Using Deep Learning Classifier.

The purpose of this study was to assess the performance of a deep learning classifier for the detection of glaucomatous change based on SD-OCT. Three hundred fifty image sets of ganglion cell-inner plexiform layer (GCIPL) and retinal nerve fiber layer (RNFL) SD-OCT for 86 glaucomatous eyes and 307 SD-OCT image sets of 196 healthy participants were recruited and split into training (197 eyes) and test (85 eyes) datasets based on a patient-wise split. The bottleneck features extracted from the GCIPL thickness map, GCIPL deviation map, RNFL thickness map, and RNFL deviation map were used as predictors for the deep learning classifier. The area under the receiver operating characteristic curve (AUC) was calculated and compared with those of conventional glaucoma diagnostic parameters including SD-OCT thickness profile and standard automated perimetry (SAP) to evaluate the accuracy of discrimination for each algorithm. In the test dataset, this deep learning system achieved an AUC of 0.990 [95% confidence interval (CI), 0.975-1.000] with a sensitivity of 94.7% and a specificity of 100.0%, which was significantly larger than the AUCs with all of the optical coherence tomography and SAP parameters: 0.949 (95% CI, 0.921-0.976) with average GCIPL thickness (P=0.006), 0.938 (95% CI, 0.905-0.971) with average RNFL thickness (P=0.003), and 0.889 (0.844-0.934) with mean deviation of SAP (P<0.001; DeLong test). An SD-OCT-based deep learning system can detect glaucomatous structural change with high sensitivity and specificity.

Separation and Thickness Measurements of Superficial and Deep Slabs of the Retinal Nerve Fiber Layer in Healthy and Glaucomatous Eyes

Describe a new method to analyze retinal nerve fiber layer (RNFL) thickness maps. Cross-sectional study. RNFL thickness maps of healthy and glaucomatous eyes. Optical coherence tomography (OCT) RNFL raster scans from 98 healthy and 50 glaucomatous eyes were analyzed. The RNFL thickness maps were separated into superficial (SNFL) and deep (DNFL) slabs through a partial thickness plane set at the modal thickness (mode). Association between mode and OCT signal strength (SS), age, axial length, and visual field mean deviation (VFMD) was tested (Pearson coefficient, r). Thicknesses of inferior and superior SNFL regions (i-,s-SNFL), and inferior, superior, nasal, and temporal DNFL regions (i-,s-,n-,t-DNFL) were calculated. The regions thicknesses were compared between healthy and glaucomatous eyes (t-test) and between glaucomatous eyes with early, moderate, and severe disease (ANOVA and linear regressions of thickness on VFMD). Diagnostic accuracy and correlation with VFMD of RNFL regions thicknesses were calculated as the area under the receiver operating characteristic curve (AUC) and Pearson r, respectively. P<0.05 was considered significant. Thickness of regions in SNFL and DNFL slabs. The mode was not associated with SS, age, axial length, or VFMD, it circumscribed the thicker RNFL around the optic disc of healthy and glaucomatous eyes, and it was used to separate the SNFL and DNFL slabs of RNFL thickness maps. The thickness of the SNFL slab was less in glaucomatous eyes than in healthy eyes (P<0.001). S-SNFL and i-SNFL thicknesses (respectively) were 86.0±8.2μm and 87.3±9.6μm in healthy eyes vs. 66.1±9.1μm and 63.4±8.2μm in glaucomatous eyes (P<0.001 for both). The thickness of the DNFL slab was similar between groups (P=0.19). T-DNFL thickness was 37.0±5.3μm in healthy eyes vs. 33.9±5.0μm in glaucomatous eyes (P<0.001); thicknesses of all other DNFL regions were similar. The SNFL regions only thinned with progressively worse glaucoma severity, had excellent AUCs (AUC≥0.95, P<0.001), and correlated strongly with VFMD (r≥0.60, P<0.001). Glaucomatous RNFL thinning is predominantly detected within a slab with thickness greater than the mode. SNFL thickness has great AUC and correlation with VFMD in glaucomatous eyes. The usefulness for diagnosis and monitoring of glaucoma needs further study.

Machine Learning Models for Diagnosing Glaucoma from Retinal Nerve Fiber Layer Thickness Maps

To assess the diagnostic accuracy of multiple machine learning models using full retinal nerve fiber layer (RNFL) thickness maps in detecting glaucoma. Case-control study. A total of 93 eyes from 69 patients with glaucoma and 128 eyes from 128 age- and sex-matched healthy controls from the Los Angeles Latino Eye Study (LALES), a large population-based, longitudinal cohort study consisting of Latino participants aged ≥40 years residing in El Puente, California. The 6×6-mm RNFL thickness maps centered on the optic nerve head (Cirrus 4000; Zeiss, Dublin, CA) were supplied to 4 different machine learning algorithms. These models included 2 conventional machine learning algorithms, Support Vector Machine (SVM) and K-Nearest Neighbor (KNN), and 2 convolutional neural nets, ResNet-18 and GlaucomaNet, which was a custom-made deep learning network. All models were tested with 5-fold cross validation. Area under the curve (AUC) statistics to assess diagnostic accuracy of each model compared with conventional average circumpapillary RNFL thickness. All 4 models achieved similarly high diagnostic accuracies, with AUC values ranging from 0.91 to 0.92. These values were significantly higher than those for average circumpapillary RNFL thickness, which had an AUC of 0.76 in the same patient population. Superior diagnostic performance was achieved with both conventional machine learning and convolutional neural net models compared with circumpapillary RNFL thickness. This supports the importance of the spatial structure of RNFL thickness map data in diagnosing glaucoma and further efforts to optimize our use of this data.

Glaucoma Diagnosis with Machine Learning Based on Optical Coherence Tomography and Color Fundus Images.

This study aimed to develop a machine learning-based algorithm for glaucoma diagnosis in patients with open-angle glaucoma, based on three-dimensional optical coherence tomography (OCT) data and color fundus images. In this study, 208 glaucomatous and 149 healthy eyes were enrolled, and color fundus images and volumetric OCT data from the optic disc and macular area of these eyes were captured with a spectral-domain OCT (3D OCT-2000, Topcon). Thickness and deviation maps were created with a segmentation algorithm. Transfer learning of convolutional neural network (CNN) was used with the following types of input images: (1) fundus image of optic disc in grayscale format, (2) disc retinal nerve fiber layer (RNFL) thickness map, (3) macular ganglion cell complex (GCC) thickness map, (4) disc RNFL deviation map, and (5) macular GCC deviation map. Data augmentation and dropout were performed to train the CNN. For combining the results from each CNN model, a random forest (RF) was trained to classify the disc fundus images of healthy and glaucomatous eyes using feature vector representation of each input image, removing the second fully connected layer. The area under receiver operating characteristic curve (AUC) of a 10-fold cross validation (CV) was used to evaluate the models. The 10-fold CV AUCs of the CNNs were 0.940 for color fundus images, 0.942 for RNFL thickness maps, 0.944 for macular GCC thickness maps, 0.949 for disc RNFL deviation maps, and 0.952 for macular GCC deviation maps. The RF combining the five separate CNN models improved the 10-fold CV AUC to 0.963. Therefore, the machine learning system described here can accurately differentiate between healthy and glaucomatous subjects based on their extracted images from OCT data and color fundus images. This system should help to improve the diagnostic accuracy in glaucoma.

Integrating Macular Ganglion Cell Inner Plexiform Layer and Parapapillary Retinal Nerve Fiber Layer Measurements to Detect Glaucoma Progression

To investigate the temporal relationship among progressive macular ganglion cell inner plexiform layer (GCIPL) thinning, progressive parapapillary retinal nerve fiber layer (RNFL) thinning, and visual field (VF) progression in patients with primary open-angle glaucoma (POAG). Prospective study. One hundred thirty-six POAG patients (231 eyes) followed up for ≥5 years. OCT imaging of the macular GCIPL and parapapillary RNFL and perimetry were performed at ∼ 4-month intervals. Progressive GCIPL and RNFL thinning were determined by Guided Progression Analysis (GPA) of serial GCIPL and RNFL thickness maps. The specificities of GPA were calculated from the proportions of eyes with progressive GCIPL or RNFL thinning in 67 eyes of 36 healthy individuals followed up for ≥5 years. Visual field progression (likely or possible) was determined by the Early Manifest Glaucoma Trial criteria. Hazard ratios for VF progression, progressive RNFL thinning, and progressive GCIPL thinning, as determined by time-varying Cox models. GPA detected 57 eyes (24.7%) with progressive GCIPL thinning and 66 eyes (28.6%) with progressive RNFL thinning at a specificity of 95.5% and 91.0%, respectively. Thirty-five eyes (15.2%) demonstrated progressive RNFL and GCIPL thinning, whereas 53 eyes (22.9%) demonstrated progressive RNFL or GCIPL thinning. Eyes with progressive GCIPL thinning had a higher risk for progressive RNFL thinning (HR, 5.27; 95% confidence interval [CI], 2.89-9.62), whereas eyes with progressive RNFL thinning were also at a higher risk for progressive GCIPL thinning (HR, 2.99; 95% CI, 1.48-6.02), after adjusting for baseline covariates. The HRs for likely and possible VF progression were 3.48 (95% CI, 1.51-8.01) and 2.74 (95% CI, 1.26-5.98), respectively, on detection of progressive GCIPL thinning and 3.66 (95% CI, 1.68-7.97) and 2.54 (95% CI, 1.23-5.21), respectively, on detection of progressive RNFL thinning after adjusting for baseline covariates. Eyes with VF progression were not at risk of progressive RNFL or GCIPL thinning (P ≥ 0.493). Progressive macular GCIPL thinning and progressive parapapillary RNFL thinning are mutually predictive. Because progressive RNFL thinning and progressive GCIPL thinning are both indicative of VF progression, integrating macular GCIPL and parapapillary RNFL measurements is relevant to facilitate early detection of disease deterioration in glaucoma patients.

Factors Associated With Visual Field Progression in Cirrus Optical Coherence Tomography-guided Progression Analysis: A Topographic Approach.

To identify factors associated with visual field (VF) progression in optical coherence tomography (OCT)-guided progression analysis (GPA) using a topographic approach. Topographic components of OCT-GPA maps (Cirrus HD-OCT; Carl Zeiss Meditec) were classified according to location (temporal, superotemporal, superonasal, nasal, inferornasal, and inferotemporal), size (small, medium, and large), shape (wedge and irregular types), and pattern of retinal nerve fiber layer (RNFL) progression (widening, deepening, and new development). All positive findings in OCT-GPA (RNFL thickness maps, profiles, and average RNFL thickness) were defined as strong RNFL progression. VF progression was determined by linear regression analysis of VF mean deviation over time. Univariate and multivariate logistic regression analyses were performed to evaluate the association of VF progression with OCT-GPA parameters. In total, 276 primary open-angle glaucoma patients were followed up for 5.1 years. According to OCT-GPA, 89 eyes were found to have RNFL progression. Of these, VF progression was detected in 27 (30.3%) eyes. Eyes with progressed VF group showed topographically different characteristics, which were wedge-shaped (80.6%), large-sized (40.0%), and widening (69.5%) or deepening (11.1%) pattern of RNFL progression in the inferotemporal (44.4%) and superotemporal (30.6%) regions, compared with eyes with non-progressed VF group. In multivariate analysis, strong RNFL progression and widening or deepening pattern of RNFL progression were significantly associated with VF progression (P=0.012 and 0.016, respectively). Topographic analysis in OCT-GPA maps showed different characteristics between progressed VF and nonprogressed VF groups. Glaucoma patients with widening or deepening pattern of RNFL progression should be carefully monitored for greater risk of VF progression.

Diagnostic Ability of Wide-field Retinal Nerve Fiber Layer Maps Using Swept-Source Optical Coherence Tomography for Detection of Preperimetric and Early Perimetric Glaucoma.

To evaluate the diagnostic ability of wide-field retinal nerve fiber layer (RNFL) maps with swept-source optical coherence tomography (SS-OCT) for detection of preperimetric (PPG) and early perimetric glaucoma (EG). One hundred eighty-four eyes, including 67 healthy eyes, 43 eyes with PPG, and 74 eyes with EG, were analyzed. Patients underwent a comprehensive ocular examination including red-free RNFL photography, visual field testing and wide-field SS-OCT scanning (DRI-OCT-1 Atlantis; Topcon, Tokyo, Japan). SS-OCT provides a wide-field RNFL thickness map and a SuperPixel map, which are composed of the RNFL deviation map of the peripapillary area and the deviation map of the composition of the ganglion cell layer with the inner plexiform layer and RNFL (GC-IPL+RNFL) in the macular area. The ability to discriminate PPG and EG from healthy eyes was assessed using sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) for all parameters and criteria provided by the wide-field SS-OCT scan. The wide-field RNFL thickness map using SS-OCT showed the highest sensitivity of PPG-diagnostic and EG-diagnostic performance compared with the other SS-OCT criteria based on the internal normative base (93.0 and 97.3%, respectively). Among the SS-OCT continuous parameters, the RFNL thickness of the 7 clock-hour, inferior and inferotemporal macular ganglion cell analyses showed the largest AUC of PPG-diagnostic and EG-diagnostic performance (AUC=0.809 to 0.865). The wide-field RNFL thickness map using SS-OCT performed well in distinguishing eyes with PPG and EG from healthy eyes. In the clinical setting, wide-field RNFL maps of SS-OCT can be useful tools for detection of early-stage glaucoma.

Trend-Based Progression Analysis for Examination of the Topography of Rates of Retinal Nerve Fiber Layer Thinning in Glaucoma

Measurement of the rates of retinal nerve fiber layer (RNFL) thinning has consisted primarily of the circumpapillary RNFL profile. This study reports the rates of RNFL thinning over the 6 × 6 mm2 RNFL thickness map and their application for indication of visual field (VF) worsening in patients with glaucoma. To investigate the association between the rates of RNFL thinning and the risk of VF worsening in patients with glaucoma. This prospective study included 117 eyes of 89 Chinese patients with primary open-angle glaucoma followed up at approximate 4-month intervals for 5 or more years between July 1, 2007, and October 30, 2015, with progressive RNFL thinning detected by optical coherence tomography trend-based progression analysis (TPA). The mean and the peak rates of RNFL thinning and the area of progressive RNFL thinning were measured by the rates of change of RNFL thickness map. Visual field worsening was determined by the Early Manifest Glaucoma Trial and pointwise linear regression criteria. Hazard ratios (HRs) for indication of VF worsening determined by time-varying Weibull survival models. Of 89 patients (117 eyes) included in the study, 53 (59.6%) were men; mean (SD) age was 54.0 (13.8) years. At the time that progressive RNFL thinning was confirmed by TPA, the mean and the peak rates of RNFL thinning were 9.06 (8.05) µm/y and 4.52 (3.19) µm/y, respectively, and the area of progressive RNFL thinning was 1.54 (1.83) mm2. The inferotemporal meridians at 268° to 288° and the superotemporal meridians at 40° to 60° were the most frequent locations where progressive RNFL thinning was observed; 41.9% of the eyes had progressive RNFL thinning at these locations. After controlling for baseline covariates, the peak and the mean rates of RNFL thinning, but not the area of progressive RNFL thinning, were indicative of VF worsening. For each micrometer-per-year increase in the peak and the mean rates of RNFL thinning, the hazard ratios were 1.12 (95% CI, 1.04-1.19) for the peak rate and 1.39 (95% CI, 1.19-1.62) for the mean rate by the Early Manifest Glaucoma Trial criteria, and 1.07 (95% CI, 1.03-1.10) for the peak rate and 1.18 (95% CI, 1.09-1.28) for the mean rate by the pointwise linear regression criteria. Topographic measurement of the rates of RNFL thinning by optical coherence tomography TPA is informative for risk assessment of VF loss in glaucoma. Although progressive RNFL thinning may not necessarily be associated with VF worsening, faster rates of RNFL thinning were associated with a higher risk of subsequent decline in VF.

Prevalence of Split Nerve Fiber Layer Bundles in Healthy People Imaged with Spectral Domain Optical Coherence Tomography.

Objectives: The presence of retinal nerve fiber layer (RNFL) split bundles was recently described in normal eyes scanned using scanning laser polarimetry and by histologic studies. Split bundles may resemble RNFL loss in healthy eyes. The aim of our study was to determine the prevalence of nerve fiber layer split bundles in healthy people.Materials and Methods: We imaged 718 eyes of 359 healthy persons with the spectral domain optical coherence tomography in this cross-sectional study. All eyes had intraocular pressure of 21 mmHg or less, normal appearance of the optic nerve head, and normal visual fields (Humphrey Field Analyzer 24-2 full threshold program). In our study, a bundle was defined as ‘split’ when there is localized defect not resembling a wedge defect in the RNFL deviation map with a symmetrically divided RNFL appearance on the RNFL thickness map. The classification was performed by two independent observers who used an identical set of reference examples to standardize the classification.Results: Inter-observer consensus was reached in all cases. Bilateral superior split bundles were seen in 19 cases (5.29%) and unilateral superior split was observed in 15 cases (4.16%). In 325 cases (90.52%) there was no split bundle.Conclusion: Split nerve fiber layer bundles, in contrast to single nerve fiber layer bundles, are not common findings in healthy eyes. In eyes with normal optic disc appearance, especially when a superior RNFL defect is observed in RNFL deviation map, the RNLF thickness map and graphs should also be examined for split nerve fiber layer bundles.

The Effect of Optic Disc Center Displacement on Retinal Nerve Fiber Layer Measurement Determined by Spectral Domain Optical Coherence Tomography.

PurposeTo investigate the effect of optic disc center displacement on retinal nerve fiber layer (RNFL) measurement determined by spectral domain optical coherence tomography (SD-OCT).MethodsThe optic disc center was manipulated at 1-pixel intervals in horizontal, vertical, and diagonal directions. According to the manipulated optic disc center location, the RNFL thickness data were resampled: (1) at a 3.46-mm diameter circle; and (2) between a 2.5-mm diameter circle and 5.4-mm square. Error was calculated between the original and resampled RNFL measurements. The tolerable error threshold of the optic disc center displacement was determined by considering test-retest variability of SD-OCT. The unreliable zone was defined as an area with 10% or more variability.ResultsThe maximum tolerable error thresholds of optic disc center displacement on the RNFL thickness map were distributed from 0.042 to 0.09 mm in 8 directions. The threshold shape was vertically elongated. Clinically important unreliable zones were located: (1) at superior and inferior region in the vertical displacement; (2) at inferotemporal region in the horizontal displacement, and (3) at superotemporal or inferotemporal region in the diagonal displacement. The unreliable zone pattern and threshold limit varied according to the direction of optic disc displacement.ConclusionsOptic disc center displacement had a considerable impact on whole RNFL thickness measurements. Understanding the effect of optic disc center displacement could contribute to reliable RNFL measurements.

Evaluation of a Myopic Normative Database for Analysis of Retinal Nerve Fiber Layer Thickness.

Analysis of retinal nerve fiber layer (RNFL) abnormalities with optical coherence tomography in eyes with high myopia has been complicated by high rates of false-positive errors. An understanding of whether the application of a myopic normative database can improve the specificity for detection of RNFL abnormalities in eyes with high myopia is relevant. To evaluate the diagnostic performance of a myopic normative database for detection of RNFL abnormalities in eyes with high myopia (spherical equivalent, -6.0 diopters [D] or less). In this cross-sectional study, 180 eyes with high myopia (mean [SD] spherical equivalent, -8.0 [1.8] D) from 180 healthy individuals were included in the myopic normative database. Another 46 eyes with high myopia from healthy individuals (mean [SD] spherical equivalent, -8.1 [1.8] D) and 74 eyes from patients with high myopia and glaucoma (mean [SD] spherical equivalent, -8.3 [1.9] D) were included for evaluation of specificity and sensitivity. The 95th and 99th percentiles of the mean and clock-hour circumpapillary RNFL thicknesses and the individual superpixel thicknesses of the RNFL thickness map measured by spectral-domain optical coherence tomography were calculated from the 180 eyes with high myopia. Participants were recruited from January 2, 2013, to December 30, 2015. The following 6 criteria of RNFL abnormalities were examined: (1) mean circumpapillary RNFL thickness below the lower 95th or (2) the lower 99th percentile; (3) one clock-hour or more for RNFL thickness below the lower 95th or (4) the lower 99th percentile; and (5) twenty contiguous superpixels or more of RNFL thickness in the RNFL thickness map below the lower 95th or (6) the lower 99th percentile. Specificities and sensitivities for detection of RNFL abnormalities. Of the 46 healthy eyes and 74 eyes with glaucoma studied (from 39 men and 38 women), the myopic normative database showed a higher specificity (63.0%-100%) than did the built-in normative database of the optical coherence tomography instrument (8.7%-87.0%) for detection of RNFL abnormalities across all the criteria examined (differences in specificities between 13.0% [95% CI, 1.1%-24.9%; P = .01] and 54.3% [95% CI, 37.8%-70.9%; P < .001]) except for the criterion of mean RNFL thickness below the lower 99th percentile, in which both normative databases had the same specificities (100%) but the myopic normative database exhibited a higher sensitivity (71.6% vs 86.5%; difference in sensitivities, 14.9% [95% CI, 4.6%-25.1%; P = .002]). The application of a myopic normative database improved the specificity without compromising the sensitivity compared with the optical coherence tomography instrument's built-in normative database for detection of RNFL abnormalities in eyes with high myopia. Inclusion of myopic normative databases should be considered in optical coherence tomography instruments.

Risk of Visual Field Progression in Glaucoma Patients with Progressive Retinal Nerve Fiber Layer Thinning: A 5-Year Prospective Study

To investigate whether progressive retinal nerve fiber layer (RNFL) thinning is predictive of progressive visual field (VF) loss in glaucoma. Prospective study. A total of 139 primary open-angle glaucoma patients (240 eyes) followed up for ≥5 years. Retinal nerve fiber layer imaging and VF testing were performed at ∼4-month intervals. Progressive RNFL thinning was determined by event analysis (Guided Progression Analysis [GPA]) and trend analysis (Trend-based Progression Analysis [TPA]) of serial registered RNFL thickness maps. VF progression was detected according to the Early Manifest Glaucoma Trial (EMGT) ("likely progression") and pointwise linear regression (PLR) criteria (≥3 contiguous locations with sensitivity change <0 decibels [dB]/year at P < 0.01). Hazard ratios (HRs) for predicting VF progression were calculated by Cox proportional hazard modeling with progressive RNFL thinning as a time-dependent covariate. The specificity of GPA/TPA for detection of RNFL changes was determined by the proportion of eyes with significant RNFL thinning/thickening in 25 normal subjects followed weekly for 8 consecutive weeks and the proportion with significant RNFL thickening in the glaucoma group. The HRs of VF progression. A total of 65 (27.1%) and 117 eyes (48.8%) had progressive RNFL thinning based on GPA and TPA, respectively, and 30 (12.5%) and 39 eyes (16.3%) had VF progression per the EMGT and PLR criteria, respectively, during follow-up. Eyes with progressive RNFL thinning had lower VF survival estimates and a faster decline of visual field index than eyes without. Progressive RNFL thinning predicted the development of VF progression with HRs of 8.44 (95% confidence interval, 3.30-21.61) (EMGT criteria) and 5.11 (2.51-10.42) (PLR criteria) for TPA and 3.95 (1.74-8.93) (EMGT criteria) and 3.81 (1.83-7.92) (PLR criteria) for GPA after controlling for baseline covariates. The specificities of GPA and TPA were 100% (83.4%-100.0%) in the normal group and 81.7% (76.2%-86.4%) and 84.2% (78.9%-88.6%), respectively, in the glaucoma group. Progressive RNFL thinning determined by GPA and TPA is predictive of detectable functional decline in glaucoma. This finding underscores the significance of detecting progressive RNFL thinning and itsrelevance to initiate or augment treatment for glaucoma patients. Regulatory authorities may consider progressive RNFL thinning as an outcome measure in clinical trials for evaluation of glaucoma treatment.

Details of Glaucomatous Damage Are Better Seen on OCT En Face Images Than on OCT Retinal Nerve Fiber Layer Thickness Maps.

High-resolution images of glaucomatous damage to the retinal nerve fiber layer (RNFL) were obtained with an adaptive optics-scanning light ophthalmoscope (AO-SLO) and used as a basis for comparisons between en face slab images and thickness maps derived from optical coherence tomography (OCT) scans. Wide-field (9 × 12 mm) cube scans were obtained with swept-source OCT (DRI-OCT) from six eyes of six patients. All eyes had a deep defect near fixation as seen on a 10-2 visual field test. Optical coherence tomography en face images, based on the average reflectance intensity, were generated (ATL 3D-Suite) from 52-μm slabs just below the vitreal border of the inner limiting membrane. The RNFL thickness maps were generated from the same OCT data. Both were compared with the AO-SLO peripapillary images that were previously obtained. On AO-SLO images, three eyes showed small regions of preserved and/or missing RNFL bundles within the affected region. Details in these regions were seen on the OCT en face images but not on the RNFL thickness maps. In addition, in the healthier hemi-retinas of two eyes, there were darker, arcuate-shaped regions on en face images that corresponded to abnormalities seen on AO-SLO. These were not seen on RNFL thickness maps. Details of local glaucomatous damage, missing or easily overlooked on traditional OCT RNFL thickness analysis used in clinical OCT reports, were seen on OCT en face images based on the average reflectance intensity. While more work is needed, it is likely that en face slab imaging has a role in the clinical management of glaucoma.

Quantitative Analysis of Localized Retinal Nerve Fiber Layer Defects Using Spectral Domain Optical Coherence Tomography

To compare the topographic features of localized retinal nerve fiber layer (RNFL) defects presented in red-free RNFL photography and spectral domain optical coherence tomography (SD-OCT), and to evaluate the correlation with structural and functional parameters. Sixty eyes with localized RNFL defects in red-free RNFL photographs were included. RNFL thickness map and significance map were obtained by SD-OCT. The angular location, angular width, and area of localized RNFL defects were measured and compared among RNFL thickness map, significance map (red, <1% level; yellow, <5% level), and RNFL photograph. The RNFL defect areas were analyzed by their correlations with structural and functional parameters. The RNFL defect area of RNFL thickness map was significantly greater than those of red significance map, and smaller than those of RNFL photograph and yellow significance map (all P<0.001). RNFL thickness map showed a significantly narrower angular width than RNFL photograph and yellow significance map (all P<0.001). The area, angular width, and angular location of localized RNFL defects in RNFL photographs strongly correlated with those of RNFL thickness maps and significance maps (all r≥0.741, P<0.001). The relationship between RNFL defect area and structural-functional parameters was also significant. The high topographic correlations in RNFL defects between RNFL photography and SD-OCT RNFL maps suggest the validity of SD-OCT RNFL imaging for detecting localized glaucomatous RNFL damage. In addition, structural and functional parameters are closely related to RNFL defect areas. Quantitative measurements of RNFL defects might be valuable for glaucoma diagnosis.

Retinal nerve fiber layer defect volume deviation analysis using spectral-domain optical coherence tomography.

To report the retinal nerve fiber layer (RNFL) defect volume deviation according to structural RNFL loss in RNFL thickness maps. Retinal nerve fiber layer defect is defined in RNFL thickness maps by the degree of RNFL loss. A 20% to 70% degree of RNFL loss was set with a 1% interval as the reference level for determining the boundary of RNFL defects. Each individual RNFL thickness map was compared with a normative database map and the region below the reference level was identified as an RNFL defect. The RNFL defect volume was calculated by summing the volumes of each pixel inside RNFL defect. The RNFL defect volume deviation was calculated by summing the differences between the normative database and the subject's RNFL measurements. To evaluate the glaucoma diagnostic ability, the areas under the receiver operating characteristics curves (AUCs) were calculated. Retinal nerve fiber layer defect volume and RNFL defect volume deviation (0.984 and 0.986, respectively) had significantly greater AUCs than all circumpapillary RNFL thickness parameters (all P < 0.001). In the early stage of RNFL loss (under 31% loss of RNFL), RNFL defect volume deviation showed better diagnostic performance than the RNFL defect volume. In multivariate analysis, RNFL defect volume and RNFL defect volume deviation were significantly associated with the mean deviation in visual field tests. Retinal nerve fiber layer defect volume deviation is a useful tool for diagnosing glaucoma and monitoring RNFL change. In early stage of RNFL loss, RNFL defect volume deviation is more sensitive for detecting glaucoma than the RNFL defect volume measurements.

Factors Determining the Peripapillary Retinal Nerve Fiber Distribution

To evaluate the influence of the arrangement of the retinal vessels and other ocular features on the peripapillary retinal nerve fiber layer (RNFL) distribution. Fifty normal eyes were included in the study. Axial length (AL), spherical equivalent of the refractive error, and central corneal thickness (CCT) were measured. The interpeak distance of the peripapillary RNFL thickness; the angle between the superior temporal (ST) and inferior temporal (IT) retinal veins; and the average peripapillary RNFL thickness were obtained from the printout results of the spectral-domain optical coherence tomography. The distance from the optic disc center to the foveola and the ratio of distance to disc width were measured from fundus photographs. All measurements were evaluated for correlations among one another and multiple regression analysis and factor analysis was used to determine how ocular factors independently related to the RNFL distribution. As the AL became longer (P=0.033), and the angle between the ST and IT veins became smaller (P<0.001), the 2 peaks of the peripapillary RNFL thickness moved farther apart. In multiple regression analysis, angle between the ST and IT veins (β=-0.587, P<0.001) and CCT (β=-0.275, P=0.011) were significantly related to interpeak distance of the peripapillary RNFL. Decreasing angle between the IT and ST veins, thinner CCT, and increasing AL all appeared to increase the separation of the RNFL peaks. Inter-vessel polar angle affected more on determining the location of the peak in RNFL thickness map. These factors might be used to optimize normative data of the peripapillary RNFL.