Dataset Information

Pattern of peripapillary capillary density loss in ischemic optic neuropathy compared to that in primary open-angle glaucoma.

ABSTRACT: Both non-arteritic anterior ischemic optic neuropathy (NAION) and primary open-angle glaucoma (POAG) damage retinal ganglion cell axons, which are perfused by the radial peripapillary capillaries. To evaluate the pattern of ischemia, we compared peripapillary capillary density (PCD) in NAION eyes to POAG eyes matched for visual field mean deviation and retinal nerve fiber layer thickness.31 chronic NAION (>6 months after the acute event) and unaffected fellow eyes (31 subjects), 42 moderate and severe POAG eyes (27 subjects), and 77 control eyes (46 healthy subjects) were imaged with a commercial optical coherence tomography angiography system (AngioVue, Avanti RTVue-XR, Optovue, CA) at two academic institutions. Two concentric circles of diameters 1.95mm (inner) and 3.45mm (outer) were manually placed on images centered on the optic nerve head, producing an annular region-of-interest. Image analysis with major vessel removal was performed using a custom program. Whole-image, whole-annulus, and sectoral PCDs were measured.Whole-image and whole-annulus PCDs in NAION and moderate and severe POAG eyes were significantly decreased compared to unaffected fellow eyes and control eyes (all P<0.001). Superior and temporal PCD values were affected more than other sectors in both NAION and POAG groups compared to control group. Whole-image and whole-annulus PCDs were not statistically different between NAION and POAG eyes (both P = 0.99). However, of all peripapillary sectors, the inferior sector PCD value was less affected in POAG eyes compared to NAION eyes (P = 0.001). Univariate analysis results also revealed a significant positive correlation between superior and inferior PCDs and corresponding RNFL thicknesses. The inferior sector correlation was greater in POAG than NAION eyes.While the whole PCD values were not different in chronic NAION and POAG, the greater correlation of inferior PCD with corresponding RNFL sectors in POAG compared to NAION suggests greater susceptibility of the inferior radial peripapillary capillary in the pathogenesis of POAG.

SUBMITTER: Fard MA

PROVIDER: S-EPMC5761857 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Pattern of peripapillary capillary density loss in ischemic optic neuropathy compared to that in primary open-angle glaucoma.

Fard Masoud Aghsaei MA Suwan Yanin Y Moghimi Sasan S Geyman Lawrence S LS Chui Toco Y TY Rosen Richard B RB Ritch Robert R

PloS one 20180110 1

<h4>Purpose</h4>Both non-arteritic anterior ischemic optic neuropathy (NAION) and primary open-angle glaucoma (POAG) damage retinal ganglion cell axons, which are perfused by the radial peripapillary capillaries. To evaluate the pattern of ischemia, we compared peripapillary capillary density (PCD) in NAION eyes to POAG eyes matched for visual field mean deviation and retinal nerve fiber layer thickness.<h4>Methods</h4>31 chronic NAION (>6 months after the acute event) and unaffected fellow eyes ...[more]

PMID: 29320503

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Project description:PurposeTo compare the fractal dimensions of the peripapillary microvasculature as obtained by optical coherence tomography angiography (OCTA) between primary open-angle glaucoma (POAG) and controls.MethodsOptic nerve head and peripapillary area images were taken using the 20° × 20°-scan of Spectralis OCTA (Heidelberg Engineering) in 97 subjects (64 POAG patients, 33 control patients). The optic nerve head microvasculature was evaluated according to predefined slabs: the superficial vascular complex (SVC) and the avascular complex (AVC). The en face image of each slab was processed by ImageJ software (National Institutes of Health) in order to calculate the vessel density and the fractal dimension using the box-counting method. For comparison, the peripapillary retinal nerve fiber layer (RNFL) thickness was obtained from Spectralis OCT circle scans. The utilities of the parameters for discriminating between the POAG and control groups were assessed using areas under the receiver operating characteristic curves (AUCs).ResultsThe SVC fractal dimension was lower in the POAG than in the control group (p < 0.001), while AVC showed no intergroup difference (p = 0.563). The fractal dimension showed a good correlation with the vessel density in both SVC and AVC (both p < 0.001). In a multivariable regression analysis, the SVC fractal dimension was negatively correlated with age (p < 0.001) and axial length (p < 0.001) and positively correlated with average RNFL thickness (p < 0.001), while the AVC fractal dimension was positively correlated with the Bruch's membrane opening size (p = 0.013). In terms of diagnostic utility, the AUC was significantly larger for the average RNFL thickness (AUC, 0.889) than for the SVC fractal dimension (AUC, 0.772; p = 0.008).ConclusionsThe fractal dimension of SVC was associated with the average RNFL thickness and was reduced in POAG patients. Fractal dimension analysis could be used in evaluating peripapillary vascularity by OCTA.

Project description:PurposePrimary open-angle glaucoma (POAG) is one of the leading causes of irreversible blindness in the United States and worldwide. Although deep learning methods have been proposed to diagnose POAG, these methods all used a single image as input. Contrastingly, glaucoma specialists typically compare the follow-up image with the baseline image to diagnose incident glaucoma. To simulate this process, we proposed a Siamese neural network, POAGNet, to detect POAG from optic disc photographs.DesignThe POAGNet, an algorithm for glaucoma diagnosis, is developed using optic disc photographs.ParticipantsThe POAGNet was trained and evaluated on 2 data sets: (1) 37 339 optic disc photographs from 1636 Ocular Hypertension Treatment Study (OHTS) participants and (2) 3684 optic disc photographs from the Sequential fundus Images for Glaucoma (SIG) data set. Gold standard labels were obtained using reading center grades.MethodsWe proposed a Siamese network model, POAGNet, to simulate the clinical process of identifying POAG from optic disc photographs. The POAGNet consists of 2 side outputs for deep supervision and uses convolution to measure the similarity between 2 networks.Main outcome measuresThe main outcome measures are the area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity.ResultsIn POAG diagnosis, extensive experiments show that POAGNet performed better than the best state-of-the-art model on the OHTS test set (area under the curve [AUC] 0.9587 versus 0.8750). It also outperformed the baseline models on the SIG test set (AUC 0.7518 versus 0.6434). To assess the transferability of POAGNet, we also validated the impact of cross-data set variability on our model. The model trained on OHTS achieved an AUC of 0.7490 on SIG, comparable to the previous model trained on the same data set. When using the combination of SIG and OHTS for training, our model achieved superior AUC to the single-data model (AUC 0.8165 versus 0.7518). These demonstrate the relative generalizability of POAGNet.ConclusionsBy simulating the clinical grading process, POAGNet demonstrated high accuracy in POAG diagnosis. These results highlight the potential of deep learning to assist and enhance clinical POAG diagnosis. The POAGNet is publicly available on https://github.com/bionlplab/poagnet.

Dataset Information

Pattern of peripapillary capillary density loss in ischemic optic neuropathy compared to that in primary open-angle glaucoma.

Publications

Pattern of peripapillary capillary density loss in ischemic optic neuropathy compared to that in primary open-angle glaucoma.

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