OCTA Multilayer and Multisector Peripapillary Microvascular Modeling for Diagnosing and Staging of Glaucoma

AI in Neuro-Ophthalmology: Current Practice and Future Opportunities

BACKGROUND

Neuro-ophthalmology frequently requires a complex and multi-faceted clinical assessment supported by sophisticated imaging techniques in order to assess disease status. The current approach to diagnosis requires substantial expertise and time. The emergence of AI has brought forth innovative solutions to streamline and enhance this diagnostic process, which is especially valuable given the shortage of neuro-ophthalmologists. Machine learning algorithms, in particular, have demonstrated significant potential in interpreting imaging data, identifying subtle patterns, and aiding clinicians in making more accurate and timely diagnosis while also supplementing nonspecialist evaluations of neuro-ophthalmic disease.

EVIDENCE ACQUISITION

Electronic searches of published literature were conducted using PubMed and Google Scholar. A comprehensive search of the following terms was conducted within the Journal of Neuro-Ophthalmology: AI, artificial intelligence, machine learning, deep learning, natural language processing, computer vision, large language models, and generative AI.

RESULTS

This review aims to provide a comprehensive overview of the evolving landscape of AI applications in neuro-ophthalmology. It will delve into the diverse applications of AI, optical coherence tomography (OCT), and fundus photography to the development of predictive models for disease progression. Additionally, the review will explore the integration of generative AI into neuro-ophthalmic education and clinical practice.

CONCLUSIONS

We review the current state of AI in neuro-ophthalmology and its potentially transformative impact. The inclusion of AI in neuro-ophthalmic practice and research not only holds promise for improving diagnostic accuracy but also opens avenues for novel therapeutic interventions. We emphasize its potential to improve access to scarce subspecialty resources while examining the current challenges associated with the integration of AI into clinical practice and research.

The Role of Optical Coherence Tomography Angiography in Glaucoma

Glaucoma is the leading cause of irreversible vision loss and comprises a group of chronic optic neuropathies characterized by progressive retinal ganglion cell (RGC) loss. Various etiologies, including impaired blood supply to the optic nerve, have been implicated for glaucoma pathogenesis. Optical coherence tomography angiography (OCTA) is a non-invasive imaging modality for visualizing the ophthalmic microvasculature. Using blood flow as an intrinsic contrast agent, it distinguishes blood vessels from the surrounding tissue. Vessel density (VD) is mainly used as a metric for quantifying the ophthalmic microvasculature. The key anatomic regions for OCTA in glaucoma are the optic nerve head area including the peripapillary region, and the macular region. Specifically, VD of the superficial peripapillary and superficial macular microvasculature is reduced in glaucoma patients compared to unaffected subjects, and VD correlates with functional deficits measured by visual field (VF). This renders OCTA similar in diagnostic capabilities compared to structural retinal nerve fiber layer (RNFL) thickness measurements, especially in early glaucoma. Furthermore, in cases where RNFL thickness measurements are limited due to artifact or floor effect, OCTA technology can be used to evaluate and monitor glaucoma, such as in eyes with high myopia and eyes with advanced glaucoma. However, the clinical utility of OCTA in glaucoma management is limited due to the prevalence of imaging artifacts. Overall, OCTA can play a complementary role in structural OCT imaging and VF testing to aid in the diagnosis and monitoring of glaucoma.

Application of artificial intelligence in glaucoma care: An updated review

The application of artificial intelligence (AI) in ophthalmology has been increasingly explored in the past decade. Numerous studies have shown promising results supporting the utility of AI to improve the management of ophthalmic diseases, and glaucoma is of no exception. Glaucoma is an irreversible vision condition with insidious onset, complex pathophysiology, and chronic treatment. Since there remain various challenges in the clinical management of glaucoma, the potential role of AI in facilitating glaucoma care has garnered significant attention. In this study, we reviewed the relevant literature published in recent years that investigated the application of AI in glaucoma management. The main aspects of AI applications that will be discussed include glaucoma risk prediction, glaucoma detection and diagnosis, visual field estimation and pattern analysis, glaucoma progression detection, and other applications.

Optical coherence tomography angiography in glaucoma

The use of optical coherence tomography angiography (OCTA) holds significant promise for optometrists in the diagnosis and management of glaucoma. It offers reliable differentiation of glaucomatous eyes from healthy ones and extends monitoring capabilities for advanced cases. OCTA represents a valuable addition to traditional assessment methods, particularly in complex cases. Glaucoma, a major cause of irreversible blindness, is traditionally diagnosed using structural and functional metrics. With growing interest, OCTA is being explored to diagnose, monitor, and manage glaucoma. This review focuses on the application of OCTA in glaucoma patients. A database search was carried out using Embase Elsevier (n = 664), PubMed (n = 574), and Cochrane Central Register of Controlled Trials (n = 19) on 15 August 2023. After deduplication and screening, 272 original papers were included in the narrative review. Inclusion criteria comprised English-language original studies on OCTA use in human glaucoma patients, with or without healthy controls. Exclusion criteria encompassed animal studies, in-vivo/in-vitro research, reviews, and congress abstracts. OCTA has good repeatability and reproducibility. OCTA metrics have good discriminatory power to differentiate glaucomatous eyes from healthy eyes and show strong associations with structural changes and visual field defects. OCTA can extend the monitoring of advanced glaucoma, addressing the 'floor effect' of traditional structural measurements. OCTA metrics can be affected by the choice of OCTA machine, post-image processing algorithms, systemic diseases, and ocular factors. Image artefacts can affect the accuracy of OCTA measurements, and proper scan quality evaluation is crucial to ensure reliable results. Additionally, artificial intelligence techniques offer promise for enhancing the diagnostic accuracy of OCTA by combining data from various retinal layers and regions. OCTA complements traditional methods in assessing glaucoma, especially in challenging cases, providing valuable insights for detection and management. Further research and clinical validation are needed to integrate OCTA into routine practice.

Vessel Density Features of Optical Coherence Tomography Angiography for Classification of Optic Neuropathies Using Machine Learning

BACKGROUND

To evaluate the classification performance of machine learning based on the 4 vessel density features of peripapillary optical coherence tomography angiography (OCT-A) for classifying healthy, nonarteritic anterior ischemic optic neuropathy (NAION), and optic neuritis (ON) eyes.

METHODS

Forty-five eyes of 45 NAION patients, 32 eyes of 32 ON patients, and 76 eyes of 76 healthy individuals with optic nerve head OCT-A were included. Four vessel density features of OCT-A images were developed using a threshold-based segmentation method and were integrated in 3 models of machine learning classifiers. Classification performances of support vector machine (SVM), random forest, and Gaussian Naive Bayes (GNB) models were evaluated with the area under the receiver-operating-characteristic curve (AUC) and accuracy.

RESULTS

We divided 121 images into a 70% training set and 30% test set. For ON-NAION classification, best results were achieved with 50% threshold, in which 3 classifiers (SVM, RF, and GNB) discriminated ON from NAION with an AUC of 1 and accuracy of 1. For ON-Normal classification, with 100% threshold, SVM and RF classifiers were able to discriminate normal from ON with AUCs of 1 and accuracies of 1. For NAION-normal classification, with 50% threshold, the SVM and RF classified the NAION from normal with AUC and accuracy of 1.

CONCLUSIONS

ML based on the combined peripapillary vessel density features of total vessels and capillaries in the whole image and ring image could provide excellent performance for NAION and ON distinction.

Vessel Density Features of Optical Coherence Tomography Angiography for Classification of Glaucoma Using Machine Learning

PRCIS

Machine learning (ML) based on the optical coherence tomography angiography vessel density features with different thresholds using a support vector machine (SVM) model provides excellent performance for glaucoma detection.

BACKGROUND

To assess the classification performance of ML based on the 4 vessel density features of peripapillary optical coherence tomography angiography for glaucoma detection.

METHODS

Images from 119 eyes of 119 glaucoma patients and 76 eyes of 76 healthy individuals were included. Four vessel density features of optical coherence tomography angiography images were developed using a threshold-based segmentation method and were integrated into 3 models of machine learning classifiers. Images were divided into 70% training set and 30% test set. Classification performances of SVM, random forest, and Gaussian Naive Bayes models were evaluated with the area under the receiver operating characteristic curve (AUC) and accuracy.

RESULTS

Glaucoma eyes had lower vessel densities at different thresholds. For differentiating glaucoma eyes, the best results were achieved with 70% and 100% thresholds, in which SVM classifier discriminated glaucoma from healthy eyes with an AUC of 1 and accuracy of 1. After SVM, the random forest classifier with 100% thresholds showed an AUC of 0.993 and an accuracy of 0.994. Furthermore, the AUC of our ML performance (SVM) was 0.96 in a subgroup analysis of mild and moderate glaucoma eyes.

CONCLUSIONS

ML based on the combined peripapillary vessel density features of total vessels and capillaries in the whole image and ring image could provide excellent performance for glaucoma detection.

Retinal OCT speckle as a biomarker for glaucoma diagnosis and staging

This paper presents a novel image analysis strategy that increases the potential of macular Optical Coherence Tomography (OCT) by using speckle features as biomarkers in different stages of glaucoma. A large pool of features (480) were computed for a subset of macular OCT volumes of the Leuven eye study cohort. The dataset contained 258 subjects that were divided into four groups based on their glaucoma severity: Healthy (56), Mild (94), Moderate (48), and Severe (60). The OCT speckle features were categorized as statistical properties, statistical distributions, contrast, spatial gray-level dependence matrices, and frequency domain features. The averaged thicknesses of ten retinal layers were also collected. Kruskal-Wallis H test and multivariable regression models were used to infer the most significant features related to glaucoma severity classification and to the correlation with visual field mean deviation. Four features were selected as being the most relevant: the ganglion cell layer (GCL) and the inner plexiform layer (IPL) thicknesses, and two OCT speckle features, the data skewness computed on the retinal nerve fiber layer (RNFL) and the scale parameter (a) of the generalized gamma distribution fitted to the GCL data. Based on a significance level of 0.05, the regression models revealed that RNFL skewness exhibited the highest significance among the features considered for glaucoma severity staging (p-values of 8.6×10-6 for the logistic model and 2.8×10-7 for the linear model). Furthermore, it demonstrated a strong negative correlation with the visual field mean deviation (ρ=-0.64). The post hoc analysis revealed that, when distinguishing healthy controls from glaucoma subjects, GCL thickness is the most relevant feature (p-value of 8.7×10-5). Conversely, when comparing the Mild versus Moderate stages of glaucoma, RNFL skewness emerged as the only feature exhibiting statistical significance (p-value = 0.001). This work shows that macular OCT speckle contains information that is currently not used in clinical practice, and not only complements structural measurements (thickness) but also has a potential for glaucoma staging.

Recognizing the Differentiation Degree of Human Induced Pluripotent Stem Cell-Derived Retinal Pigment Epithelium Cells Using Machine Learning and Deep Learning-Based Approaches

Induced pluripotent stem cells (iPSCs) can be differentiated into mesenchymal stem cells (iPSC-MSCs), retinal ganglion cells (iPSC-RGCs), and retinal pigmental epithelium cells (iPSC-RPEs) to meet the demand of regeneration medicine. Since the production of iPSCs and iPSC-derived cell lineages generally requires massive and time-consuming laboratory work, artificial intelligence (AI)-assisted approach that can facilitate the cell classification and recognize the cell differentiation degree is of critical demand. In this study, we propose the multi-slice tensor model, a modified convolutional neural network (CNN) designed to classify iPSC-derived cells and evaluate the differentiation efficiency of iPSC-RPEs. We removed the fully connected layers and projected the features using principle component analysis (PCA), and subsequently classified iPSC-RPEs according to various differentiation degree. With the assistance of the support vector machine (SVM), this model further showed capabilities to classify iPSCs, iPSC-MSCs, iPSC-RPEs, and iPSC-RGCs with an accuracy of 97.8%. In addition, the proposed model accurately recognized the differentiation of iPSC-RPEs and showed the potential to identify the candidate cells with ideal features and simultaneously exclude cells with immature/abnormal phenotypes. This rapid screening/classification system may facilitate the translation of iPSC-based technologies into clinical uses, such as cell transplantation therapy.

A comprehensive update on the use of optical coherence tomography angiography in glaucoma

PURPOSE

The primary purpose of this review is to provide a comprehensive summary on the technical principles of OCTA and to enumerate vascular parameters being explicated for glaucoma diagnosis and progression with emphasis on recent studies. In addition, the authors also summarize the future clinical potentials of OCTA in glaucoma and enumerate the limitations of this imaging modality in the present-day scenario.

METHODS

The index study is a narrative review on OCTA in glaucoma. The authors searched the PubMed database using the key phrases ''optical coherence tomography angiography" AND "glaucoma,'' AND/OR "vascular parameters" AND/OR "ocular perfusion." Being a relatively recent development in ocular imaging, studies in which OCTA imaging had been used for glaucoma evaluation since 2012 were included until March 2022. The literature search included original studies and previous review articles, while case reports were excluded. Preliminary search was based on relevant articles with search keywords in the title and abstract. The second screening was performed by reading the full text of the literature.

RESULTS

Recent studies indicate reduction in microcirculation in glaucomatous eyes compared to the normal subjects. The area of interest for glaucoma evaluation using OCTA varies among the different studies. Based on the literature reviewed here, (1) OCTA parameters measured in the peripapillary; ONH and macular area have been shown to differentiate between glaucoma and normal eyes with a discriminatory power comparable to OCT parameters used routinely in clinics, (2) monitoring of peripapillary and macular vessel density may provide important information to the evaluation of glaucoma progression and prediction of rates of disease worsening, (3) studies suggest strong correlation between the OCTA parameters, the OCT parameters and visual function, measured by visual field testing, in glaucomatous eyes, (4) future prospects of OCTA in glaucoma evaluations using AI predicting structural and functional features and prognosis based on early vascular findings would open up scope for early detection of high-risk suspects and fast progressors in glaucoma.

CONCLUSION

OCTA can be useful in quantifying vascular parameters in the optic disc, peripapillary and the macular regions for glaucoma evaluation. OCTA shows potential to become a part of everyday glaucoma management.

Analysis of the Optic Nerve Head Microcirculation Using Optical Coherence Tomography Angiography and the Upstream Macrocirculation Using Color Doppler Imaging in Low-Tension and High-Tension Glaucoma Patients

INTRODUCTION

The aim of the study was to analyze the optic nerve head (ONH) microcirculation using optical coherence tomography angiography (OCT-A) and the upstream macrocirculation using color Doppler imaging (CDI) in low-tension and high-tension glaucoma (LTG and HTG, respectively).

METHODS

This cross-sectional study included 67 eyes of 67 HTG patients, 55 eyes of 55 LTG patients, and 42 eyes of 42 healthy controls. We recorded the complete ophthalmological examination, visual fields, retinal nerve fiber layer (RNFL) thickness, ONH vessel density (VD) measured using OCT-A, peak systolic velocity (PSV), end-diastolic velocity (EDV), and resistive index (RI) measured using CDI. SPSS software was used for data analysis. Data are presented as mean ± standard deviation or median (interquartile range) and compared using t test or Mann-Whitney U test, as appropriate. Pearson χ2 test or Fisher's exact test was used for comparisons, as appropriate. Pearson correlation analysis was used to evaluate the correlations between variables. p < 0.05 was considered statistically significant.

RESULTS

The ONH VD and RNFL thickness were considerably lower in glaucomatous eyes than in healthy eyes (both p < 0.001). Compared with the HTG group, the LTG group had lower VD in the peripapillary region (p = 0.027). Compared with the healthy group, the HTG group had lower PSV (p = 0.029 and = 0.023, respectively), lower EDV (p = 0.023 and <0.001, respectively), and higher RI (p = 0.019 and = 0.006, respectively) of the internal carotid artery (ICA) and central retinal artery (CRA). The LTG group had lower PSV (p = 0.015 and <0.001, respectively) and EDV (p = 0.047 and = 0.001, respectively) of the ophthalmic artery (OA) and CRA. The LTG group had lower PSV of CRA than the HTG group (p = 0.034). In glaucomatous eyes, peripapillary VD had a significant association with the mean defect (p < 0.001) and RNFL thickness (p < 0.001), but not with the other CDI indices (all p > 0.05).

CONCLUSION

The ONH microcirculation and upstream macrocirculation of the large arteries exhibited differences in the blood flow characteristics between the LTG and HTG groups. These differences may improve our understanding of glaucoma. There was no correlation between the characteristics of the ONH microcirculation and the upstream macrocirculation of large vessels in the LTG and HTG groups.

The Development and Clinical Application of Innovative Optical Ophthalmic Imaging Techniques

The field of ophthalmic imaging has grown substantially over the last years. Massive improvements in image processing and computer hardware have allowed the emergence of multiple imaging techniques of the eye that can transform patient care. The purpose of this review is to describe the most recent advances in eye imaging and explain how new technologies and imaging methods can be utilized in a clinical setting. The introduction of optical coherence tomography (OCT) was a revolution in eye imaging and has since become the standard of care for a plethora of conditions. Its most recent iterations, OCT angiography, and visible light OCT, as well as imaging modalities, such as fluorescent lifetime imaging ophthalmoscopy, would allow a more thorough evaluation of patients and provide additional information on disease processes. Toward that goal, the application of adaptive optics (AO) and full-field scanning to a variety of eye imaging techniques has further allowed the histologic study of single cells in the retina and anterior segment. Toward the goal of remote eye care and more accessible eye imaging, methods such as handheld OCT devices and imaging through smartphones, have emerged. Finally, incorporating artificial intelligence (AI) in eye images has the potential to become a new milestone for eye imaging while also contributing in social aspects of eye care.

Comparison of Retinal Nerve Fiber Layer and Ganglion Cell-Inner Plexiform Layer Thickness Values Using Spectral-Domain and Swept-Source OCT

Purpose

To compare peripapillary retinal nerve fiber layer (pRNFL) and macular ganglion cell-inner plexiform layer (mGCIPL) thickness measurements obtained with spectral domain optical coherence tomography (SD-OCT) and swept-source OCT (SS-OCT) using an OCT-angiography scanning protocol, and their ability to distinguish among patients with glaucoma, glaucoma suspects (GS), and healthy controls (HC).

Methods

Cross-sectional study of 196 eyes (81 glaucoma, 48 GS, and 67 HC) of 119 participants. Participants underwent peripapillary and macular OCT with SD-OCT and SS-OCT. Parameters of interest were average and sector-wise pRNFL and mGCIPL thickness. Inter-device agreement was investigated with Bland-Altman statistics. Conversion formulas were developed with linear regression. Diagnostic performances were evaluated with area under the receiver operating characteristic curves.

Results

Both SD-OCT and SS-OCT detected a significant pRNFL and mGCIPL thinning in glaucoma patients compared to HC and GS for almost all study sectors. A strong linear relationship between the two devices was present for all quadrants/sectors (R2 ≥ 0.81, P < 0.001), except for the nasal (R2 = 0.49, P < 0.001) and temporal (R2 = 0.62, P < 0.001) pRNFL quadrants. SD-OCT and SS-OCT measurements had a proportional bias, which could be removed with conversion formulas. Overall, the two devices showed similar diagnostic abilities.

Conclusions

Thickness values obtained with SD-OCT and SS-OCT are not directly interchangeable but potentially interconvertible. Both devices have a similar ability to discriminate glaucoma patients from GS and healthy subjects.

Translational Relevance

OCT-Angiography scans can be reliably used to obtain structural metrics in glaucoma patients.

Towards standardizing retinal optical coherence tomography angiography: a review

The visualization and assessment of retinal microvasculature are important in the study, diagnosis, monitoring, and guidance of treatment of ocular and systemic diseases. With the introduction of optical coherence tomography angiography (OCTA), it has become possible to visualize the retinal microvasculature volumetrically and without a contrast agent. Many lab-based and commercial clinical instruments, imaging protocols and data analysis methods and metrics, have been applied, often inconsistently, resulting in a confusing picture that represents a major barrier to progress in applying OCTA to reduce the burden of disease. Open data and software sharing, and cross-comparison and pooling of data from different studies are rare. These inabilities have impeded building the large databases of annotated OCTA images of healthy and diseased retinas that are necessary to study and define characteristics of specific conditions. This paper addresses the steps needed to standardize OCTA imaging of the human retina to address these limitations. Through review of the OCTA literature, we identify issues and inconsistencies and propose minimum standards for imaging protocols, data analysis methods, metrics, reporting of findings, and clinical practice and, where this is not possible, we identify areas that require further investigation. We hope that this paper will encourage the unification of imaging protocols in OCTA, promote transparency in the process of data collection, analysis, and reporting, and facilitate increasing the impact of OCTA on retinal healthcare delivery and life science investigations.

The use of deep learning technology for the detection of optic neuropathy

The emergence of computer graphics processing units (GPUs), improvements in mathematical models, and the availability of big data, has allowed artificial intelligence (AI) to use machine learning and deep learning (DL) technology to achieve robust performance in various fields of medicine. The DL system provides improved capabilities, especially in image recognition and image processing. Recent progress in the sorting of AI data sets has stimulated great interest in the development of DL algorithms. Compared with subjective evaluation and other traditional methods, DL algorithms can identify diseases faster and more accurately in diagnostic tests. Medical imaging is of great significance in the clinical diagnosis and individualized treatment of ophthalmic diseases. Based on the morphological data sets of millions of data points, various image-related diagnostic techniques can now impart high-resolution information on anatomical and functional changes, thereby providing unprecedented insights in ophthalmic clinical practice. As ophthalmology relies heavily on imaging examinations, it is one of the first medical fields to apply DL algorithms in clinical practice. Such algorithms can assist in the analysis of large amounts of data acquired from the examination of auxiliary images. In recent years, rapid advancements in imaging technology have facilitated the application of DL in the automatic identification and classification of pathologies that are characteristic of ophthalmic diseases, thereby providing high quality diagnostic information. This paper reviews the origins, development, and application of DL technology. The technical and clinical problems associated with building DL systems to meet clinical needs and the potential challenges of clinical application are discussed, especially in relation to the field of optic nerve diseases.

Combining Structural and Vascular Parameters to Discriminate Among Glaucoma Patients, Glaucoma Suspects, and Healthy Subjects

Purpose

Compare the ability of peripapillary and macular structural parameters, vascular parameters, and their integration to discriminate among glaucoma, suspected glaucoma (GS), and healthy controls (HCs).

Methods

In this study, 196 eyes of 119 patients with glaucoma (n = 81), patients with GS (n = 48), and HCs (n = 67) underwent optical coherence tomography (OCT) and OCT angiography to measure peripapillary retinal nerve fiber layer (pRNFL), macular ganglion cell-inner plexiform layer (mGCIPL) thicknesses, radial peripapillary capillary perfusion density (RPC-PD), and macular GCIPL perfusion density (GCIPL-PD). Parameters were integrated regionally with logistic regression and globally with machine learning algorithms. Diagnostic performances were evaluated with area under the receiver operating characteristic (AUROC) curves.

Results

Patients with glaucoma had mild to moderate damage (median, -3.3 dB; interquartile range, -6.5 to -1.4). In discriminating between patients with glaucoma and the HCs, pRNFL thickness had higher AUROC curve values than RPC-PD for average (0.87 vs. 0.62; P < 0.001), superior (0.86 vs. 0.54; P < 0.001), inferior (0.90 vs. 0.71; P < 0.001), and temporal (0.65 vs. 0.51; P = 0.02) quadrants. mGCIPL thickness had higher AUROC curve values than GCIPL-PD for average (0.84 vs. 0.68; P < 0.001), superotemporal (0.76 vs. 0.65; P = 0.016), superior (0.72 vs. 0.57; P = 0.004), superonasal (0.70 vs. 0.56; P = 0.01), inferotemporal (0.90 vs. 0.72; P < 0.001), inferior (0.87 vs. 0.69; P < 0.001), and inferonasal (0.78 vs. 0.65, P = 0.012) sectors. All structural multisector indices had higher diagnostic ability than vascular ones (P < 0.001). Combined structural-vascular indices did not outperform structural indices. Similar results were found to discriminate glaucoma from GS.

Conclusions

Combining structural and vascular parameters in a structural-vascular index does not improve diagnostic ability over structural parameters alone.

Translational Relevance

OCT angiography does not add additional benefit to structural OCT in early to moderate glaucoma diagnosis.

Microvascular and structural alterations in the optic nerve head of advanced primary open-angle glaucoma compared with atrophic non-arteritic anterior ischemic optic neuropathy

PURPOSE

This cross-sectional study compared the peripapillary vessel density (VD), peripapillary retinal nerve fiber layer (RNFL) thickness, and optic nerve head (ONH) parameters between eyes with atrophic non-arteritic anterior ischemic optic neuropathy (NAION) and eyes with advanced primary open-angle glaucoma (POAG) matched for visual field mean deviation.

METHODS

Peripapillary VDs and RNFL thicknesses in the peripapillary region, and 4 sectors (superior, inferior, nasal, and temporal), and scanning laser ophthalmoscopy parameters of the ONH were evaluated with optical coherence tomography angiography (OCTA) among 21 atrophic NAION cases, 26 advanced POAG cases, and 30 age- and sex-matched healthy controls.

RESULTS

The POAG eyes had lower peripapillary VDs in all areas compared with the NAION eyes, which was most marked in the inferior and nasal sectors (p=0.005 for both). RNFL loss was similar between the 2 groups in all areas, except for a preserved thickness in the inferior sector in NAION eyes (p=0.01). Peripapillary VD demonstrated stronger correlations with global RNFL thickness in the peripapillary region in the NAION eyes compared with that of the POAG eyes (r=0.91 p<0.00001, r=0.42 p=0.03 respectively). In multivariate analysis, the peripapillary VD correlated with age and RNFL thickness in the POAG eyes while it correlated with SSI and RNFL thickness in the NAION eyes.

CONCLUSIONS

A tendency for a lower peripapillary VD despite similar visual field mean deviation values may infer a more prominent role of the vascular regression in POAG compared with NAION.