Foveal Avascular Zone Measurement Via Optical Coherence Tomography Angiography and its Relationship With the Visual Field in Eyes With Open-angle Glaucoma

Igarashi, Ryoko; Ochiai, Shun; Togano, Tetsuya; Sakaue, Yuta; Suetake, Aki; Iikawa, Ryu; Honma, Yurie; Miyamoto, Daiki; Fukuchi, Takeo

doi:10.1097/ijg.0000000000001492

Cited by 14 publications

(6 citation statements)

References 23 publications

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“…e knowledge of the relationship of glaucoma with some vasospasm phenomena [32,33], such as migraines [34,35], syndrome of Raynaud, and peripheral vascular dysregulation [36], and also with sleep apnea [37,38], supports the idea of the participation of vascular dysfunction in the pathogenesis of glaucoma. However, despite the recent evolution in studies that assess blood flow in the ONH and especially vascular density through OCT-A, the high cost of this technology represents a limitation of its widespread use [12,[39][40][41][42][43][44]. Within this context, as previously described, the Laguna ONhE program represents a feasible lower cost option, analyzing conventional color retinography, to evaluate the vascular component by estimating Hb levels at the ONH.…”

Section: Discussionmentioning

confidence: 99%

Optic Nerve Head Hemoglobin Levels in Glaucoma: A Structural and Functional Correlation Study

Rocha

Dias

Lemos

et al. 2021

Journal of Ophthalmology

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Purpose. To investigate structural and functional correlations in glaucoma patients using optic nerve head hemoglobin (ONH Hb) measurements as determined by automated colorimetric analysis of conventional retinography. Methods. We prospectively enrolled healthy participants and glaucomatous patients with a wide range of disease stages. All participants underwent visual field (VF) testing (standard automated perimetry, SAP), color fundus imaging (mydriatic retinography), and peripapillary retinal nerve fiber layer (pRNFL) assessment through spectral-domain optical coherence tomography (SD-OCT). Software Laguna ONhE was used to estimate the amount of ONH Hb and to determine the glaucoma discriminant function (GDF) index. Scatter plots were constructed, and regression analysis was used to investigate the correlations between GDF, average pRNFL thickness, and VF mean deviation (VFMD) index values. A secondary analysis was performed to compare each parameter between three different glaucoma groups divided according to VFMD values (mild, >−6 dB; moderate, −6 to −12 dB; and advanced, <−12 dB). Results. One hundred ninety-six eyes from 123 participants (69 with glaucoma and 54 controls) were enrolled. Overall, all parameters evaluated differed significantly between glaucomatous and control eyes ( p ≤ 0.001 ). The comparison of each parameter according to groups of disease stages revealed significant differences between controls and each of the glaucomatous groups ( p < 0.001 ). More pronounced changes in GDF values were observed in early disease stages. We found significant nonlinear correlations between GDF and VFMD values (R2 = 0.295, p < 0.001 ) and between pRNFL thickness and VFMD (R2 = 0.598, p < 0.001 ). A linear correlation was found between GDF and pRNFL thickness values (R2 = 0.195, p < 0.001 ). Conclusion. Our results showed significant associations between ONH Hb values and both structural and functional damage in glaucoma obtained by SD-OCT and SAP, respectively. The nonlinear correlation we found and the GDF behavior along different disease stages suggest that ONH Hb levels’ reduction may precede visual function changes in early glaucoma stages.

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Section: Discussionmentioning

confidence: 99%

Optic Nerve Head Hemoglobin Levels in Glaucoma: A Structural and Functional Correlation Study

Rocha

Dias

Lemos

et al. 2021

Journal of Ophthalmology

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“…• Foveal avascular zone is an area around the macula that is absent of blood vessel. Previous study found an increase in the area of the foveal avascular zone in glaucoma patients [22]. This finding was correlated with visual field abnormalities.…”

Section: Introductionmentioning

confidence: 51%

“…[43] applied modified VGG16 to the peripapillary OCTA images and found the areas under precision-recall curves of 0.97. In this study, we used wider field of OCTA image that included retinal and capillary vessels outside the peripapillary area because the correlation between foveal avascular zone and glaucoma was reported in [22]- [24]. In addition, because the prevalence of poor image quality was high [47]- [49], wider field of view that includes more clinical important area may provide more data for CNN to learn and predict in less severe case of poor quality image.…”

Section: Discussionmentioning

confidence: 99%

GlauNet: Glaucoma Diagnosis for OCTA Imaging Using a New CNN Architecture

et al. 2022

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Glaucoma is a neurodegenerative disease that affects the optic nerve head and causes visual field defect. Current investigations focus on neural component which may overlook other important factors such as the vascular cause. The optical coherence tomography angiography (OCTA) imaging has been developed and provided quantitative parameters that showed good diagnostic accuracy to detect glaucoma. However, those parameters are based on image processing of observed clinical findings, therefore, some image information can be lost. Convolutional neural network has been successfully applied for automatic feature extraction and object classification. In this study, the glaucoma diagnosis network, namely GlauNet, has been proposed. GlauNet consists of two sections: the feature-extraction section and the classification section. The feature-extraction section has three convolutional layers. Each convolutional layer is followed by rectified linear unit and maximum pooling layer. The classification section contains five fully connected layers. GlauNet was trained with 258 glaucomatous and 439 non-glaucomatous eyes. The visualization of the feature-extraction section showed the highlight in the area of optic nerve head and retinal nerve fiber layer in the superotemporal and inferotemporal regions. It was then tested on 27 glaucomatous and 48 non-glaucomatous eyes. Its sensitivity and specificity were 88.9% with 89.6%, respectively. The area under receiver operating characteristic curve of GlauNet was 0.89. GlauNet was robust against the artifacts. Its sensitivity and specificity were still higher than 80% (82.4% and 80.3%, respectively) when tested on 88 poor-quality images.

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“…The association between the FAZ area and structural and functional damage has been assessed in previous studies. In a study by Igarashi et al, the FAZ area was significantly correlated with the RNFL thickness, GCC thickness, foveal threshold and MD value [ 32 ]. Li et al showed that an enlarged FAZ area was associated with a higher risk of RNFL thinning in OAG [ 33 ].…”

Section: Discussionmentioning

confidence: 99%

A Deep Learning System Using Optical Coherence Tomography Angiography to Detect Glaucoma and Anterior Ischemic Optic Neuropathy

Bunod

Lubrano²,

Pirovano³

et al. 2023

JCM

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Introduction. Glaucoma and non-arteritic anterior ischemic optic neuropathy (NAION) are optic neuropathies that can both lead to irreversible blindness. Several studies have compared optical coherence tomography angiography (OCTA) findings in glaucoma and NAION in the presence of similar functional and structural damages with contradictory results. The goal of this study was to use a deep learning system to differentiate OCTA in glaucoma and NAION. Material and methods. Sixty eyes with glaucoma (including primary open angle glaucoma, angle-closure glaucoma, normal tension glaucoma, pigmentary glaucoma, pseudoexfoliative glaucoma and juvenile glaucoma), thirty eyes with atrophic NAION and forty control eyes (NC) were included. All patients underwent OCTA imaging and automatic segmentation was used to analyze the macular superficial capillary plexus (SCP) and the radial peripapillary capillary (RPC) plexus. We used the classic convolutional neural network (CNN) architecture of ResNet50. Attribution maps were obtained using the “Integrated Gradients” method. Results. The best performances were obtained with the SCP + RPC model achieving a mean area under the receiver operating characteristics curve (ROC AUC) of 0.94 (95% CI 0.92–0.96) for glaucoma, 0.90 (95% CI 0.86–0.94) for NAION and 0.96 (95% CI 0.96–0.97) for NC. Conclusion. This study shows that deep learning architecture can classify NAION, glaucoma and normal OCTA images with a good diagnostic performance and may outperform the specialist assessment.

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Foveal Avascular Zone Measurement Via Optical Coherence Tomography Angiography and its Relationship With the Visual Field in Eyes With Open-angle Glaucoma

Cited by 14 publications

References 23 publications

Optic Nerve Head Hemoglobin Levels in Glaucoma: A Structural and Functional Correlation Study

Optic Nerve Head Hemoglobin Levels in Glaucoma: A Structural and Functional Correlation Study

GlauNet: Glaucoma Diagnosis for OCTA Imaging Using a New CNN Architecture

A Deep Learning System Using Optical Coherence Tomography Angiography to Detect Glaucoma and Anterior Ischemic Optic Neuropathy

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