Early diabetic retinopathy diagnosis based on local retinal blood vessel analysis in optical coherence tomography angiography (OCTA) images

Artificial vascular image generation using blood vessel texture maps

BACKGROUND

Current methods for identifying blood vessels in digital images typically involve training neural networks on pixel-wise annotated data. However, manually outlining whole vessel trees in images tends to be very costly. One approach for reducing the amount of manual annotation is to pre-train networks on artificially generated vessel images. Recent pre-training approaches focus on generating proper artificial geometries for the vessels, while the appearance of the vessels is defined using general statistics of the real samples or generative networks requiring an additional training procedure to be defined. In contrast, we propose a methodology for generating blood vessels with realistic textures extracted directly from manually annotated vessel segments from real samples. The method allows the generation of artificial images having blood vessels with similar geometry and texture to the real samples using only a handful of manually annotated vessels.

METHODS

The first step of the method is the manual annotation of the borders of a small vessel segment, which takes only a few seconds. The annotation is then used for creating a reference image containing the texture of the vessel, called a texture map. A procedure is then defined to allow texture maps to be placed on top of any smooth curve using a piecewise linear transformation. Artificial images are then created by generating a set of vessel geometries using Bézier curves and assigning vessel texture maps to the curves.

RESULTS

The method is validated on a fluorescence microscopy (CORTEX) and a fundus photography (DRIVE) dataset. We show that manually annotating only 0.03% of the vessels in the CORTEX dataset allows pre-training a network to reach, on average, a Dice score of 0.87 ± 0.02, which is close to the baseline score of 0.92 obtained when all vessels of the training split of the dataset are annotated. For the DRIVE dataset, on average, a Dice score of 0.74 ± 0.02 is obtained by annotating only 0.29% of the vessels, which is also close to the baseline Dice score of 0.81 obtained when all vessels are annotated.

CONCLUSION

The proposed method can be used for disentangling the geometry and texture of blood vessels, which allows a significant improvement of network pre-training performance when compared to other pre-training methods commonly used in the literature.

Vessel-promoted OCT to OCTA image translation by heuristic contextual constraints

Optical Coherence Tomography Angiography (OCTA) is a crucial tool in the clinical screening of retinal diseases, allowing for accurate 3D imaging of blood vessels through non-invasive scanning. However, the hardware-based approach for acquiring OCTA images presents challenges due to the need for specialized sensors and expensive devices. In this paper, we introduce a novel method called TransPro, which can translate the readily available 3D Optical Coherence Tomography (OCT) images into 3D OCTA images without requiring any additional hardware modifications. Our TransPro method is primarily driven by two novel ideas that have been overlooked by prior work. The first idea is derived from a critical observation that the OCTA projection map is generated by averaging pixel values from its corresponding B-scans along the Z-axis. Hence, we introduce a hybrid architecture incorporating a 3D adversarial generative network and a novel Heuristic Contextual Guidance (HCG) module, which effectively maintains the consistency of the generated OCTA images between 3D volumes and projection maps. The second idea is to improve the vessel quality in the translated OCTA projection maps. As a result, we propose a novel Vessel Promoted Guidance (VPG) module to enhance the attention of network on retinal vessels. Experimental results on two datasets demonstrate that our TransPro outperforms state-of-the-art approaches, with relative improvements around 11.4% in MAE, 2.7% in PSNR, 2% in SSIM, 40% in VDE, and 9.1% in VDC compared to the baseline method. The code is available at: https://github.com/ustlsh/TransPro.

The application of artificial intelligence in diabetic retinopathy: progress and prospects

In recent years, artificial intelligence (AI), especially deep learning models, has increasingly been integrated into diagnosing and treating diabetic retinopathy (DR). From delving into the singular realm of ocular fundus photography to the gradual development of proteomics and other molecular approaches, from machine learning (ML) to deep learning (DL), the journey has seen a transition from a binary diagnosis of "presence or absence" to the capability of discerning the progression and severity of DR based on images from various stages of the disease course. Since the FDA approval of IDx-DR in 2018, a plethora of AI models has mushroomed, gradually gaining recognition through a myriad of clinical trials and validations. AI has greatly improved early DR detection, and we're nearing the use of AI in telemedicine to tackle medical resource shortages and health inequities in various areas. This comprehensive review meticulously analyzes the literature and clinical trials of recent years, highlighting key AI models for DR diagnosis and treatment, including their theoretical bases, features, applicability, and addressing current challenges like bias, transparency, and ethics. It also presents a prospective outlook on the future development in this domain.

Retinal vascular changes after Silicon Oil removal in the Eye with Rhegmatogenous Retinal detachment

BACKGROUND

This study aims to examine vessel density changes in the optic nerve and macula following silicone oil removal (SOR) surgery in eyes with rhegmatogenous retinal detachment (RRD) at different time points by Optical Coherence Tomography Angiography (OCTA) in compared to the contralateral eye.

METHODS

A total of 43 eyes from 43 patients with silicone oil in their eyes for 3-9 months underwent OCT-A using AngioVue and optic disc-associated vessel density (VD) and thickness, macular-associated VD and thickness, Foveal avascular zone (FAZ) area, FAZ perimeter (PERIM), Acircularity index (AI), vessel density within a 300 μm wide region of the FAZ were compared between eyes. OCTA scans were performed one week before SOR and one month and three months after SOR.

RESULTS

The mean age of participants was 52.8 years (SD = 15.85) and a median visual acuity was 0.8 (range: 0.5-1.0). Notably, male participants constituted 67.4% of the sample. The preoperative mean value BCVA (logMAR) of patients was 0.73, and 3 months post-oil removal was 0.7727. Regarding optic disc parameters, RNFL thickness and vessel density (VD) measurements Peripapillary, whole disc, inside disc, and Disc Angio (superior, Nasal, inferior, temporal) did not change. In analyzing macular thickness parameters, all of them (Whole and Fovea, parafoveal, and Perifovea) remained unchanged. Examining macular vessel density parameters revealed no significant changes across superficial and deep retinal layers. Finally, the comparison of the foveal avascular zone (FAZ) area and flow density (FD) parameters demonstrated consistent measurements with non-significant alterations observed in FAZ size (p = 0.6) and FD values (p = 0.49) over the monitored duration.

CONCLUSION

There was no change in peripapillary VD and macular vessel density of the superficial capillary plexus (SCP) and deep capillary plexus (DCP) after silicone oil removal. FAZ and full retinal thickness  remained stable 3 month after SOR.  Clinical trial number: Not applicable.

Ensembling U-Nets for microaneurysm segmentation in optical coherence tomography angiography in patients with diabetic retinopathy

Diabetic retinopathy is one of the leading causes of blindness around the world. This makes early diagnosis and treatment important in preventing vision loss in a large number of patients. Microaneurysms are the key hallmark of the early stage of the disease, non-proliferative diabetic retinopathy, and can be detected using OCT angiography quickly and non-invasively. Screening tools for non-proliferative diabetic retinopathy using OCT angiography thus have the potential to lead to improved outcomes in patients. We compared different configurations of ensembled U-nets to automatically segment microaneurysms from OCT angiography fundus projections. For this purpose, we created a new database to train and evaluate the U-nets, created by two expert graders in two stages of grading. We present the first U-net neural networks using ensembling for the detection of microaneurysms from OCT angiography en face images from the superficial and deep capillary plexuses in patients with non-proliferative diabetic retinopathy trained on a database labeled by two experts with repeats.

Variability of scan quality and perfusion density in longitudinal optical coherence tomography angiography imaging

BACKGROUND/AIMS

Optical coherence tomography angiography (OCT-A) images are subject to variability, but the extent to which learning impacts OCT-A measurements is unknown. We determined whether there is a learning effect in glaucoma patients and healthy controls imaged with OCT-A.

METHODS

Ninety-one open-angle glaucoma patients and 54 healthy controls were imaged every 4 months over a period of approximately 1 year in this longitudinal cohort study. We analysed 15°×15° scans, centred on the fovea, in one eye of each participant. Two-dimensional projection images for the superficial, intermediate and deep vascular plexuses were exported and binarised after which perfusion density was calculated. Linear mixed-effects models were used to investigate the association between perfusion density and follow-up time.

RESULTS

The mean (SD) age of glaucoma patients and healthy controls was 67.3 (8.1) years and 62.1 (9.0) years, respectively. There was a significant correlation between perfusion density and scan quality in both glaucoma patients (r=0.50 (95% CI 0.42 to 0.58); p<0.05) and healthy controls (r=0.41 (95% CI 0.29 to 0.52); p<0.05). An increase in perfusion density occurred over time and persisted, even after adjustment for scan quality (1.75% per year (95% CI 1.14 to 2.37), p<0.01).

CONCLUSIONS

Perfusion density measurements are subject to increasing experience of either the operator or participant, or a combination of both. These findings have implications for the interpretation of longitudinal measurements with OCT-A.

Relationship between nailfold capillaroscopy parameters and the severity of diabetic retinopathy

PURPOSE

To determine whether non-invasive measurements of the nailfold capillaries (NCs) are associated with the presence and severity of diabetic retinopathy (DR) in patients with type 2 diabetes.

METHODS

Eighty-three eyes of 83 patients with type 2 diabetes were enrolled. Sixty-three age-matched non-diabetic subjects served as controls. Diabetic patients were classified by the severity of their DR: non-DR (NDR), non-proliferative DR (NPDR), and proliferative DR (PDR). We used nailfold capillaroscopy to measure NC parameters, including number, length, width, and turbidity.

RESULTS

Four NC parameters in the diabetic patients were significantly lower than in the controls (all P < 0.001). There was a statistically significant decrease in the NC parameters along with the increasing severity of DR (number: P = 0.02; all others: P < 0.001). Logistic regression analysis revealed that combining the systemic characteristics of age, sex, systolic blood pressure, estimated glomerular filtration rate, hemoglobin A1c level, and history of hypertension and dyslipidemia could indicate the presence of DR and PDR (the area under the receiver operating characteristic curve [AUC] = 0.81, P = 0.006; AUC = 0.87, P = 0.001, respectively). Furthermore, the discriminative power of DR was significantly improved (P = 0.03) by adding NC length to the systemic findings (AUC = 0.89, P < 0.001).

CONCLUSION

NC measurement is a simple and non-invasive way to assess the risk of DR and its severity.

Sensitivity and specificity of optical coherence tomography angiography for diagnosis and classification of diabetic retinopathy; a systematic review and meta-analysis

BACKGROUND

Optical coherence tomography angiography (OCTA) is a noninvasive imaging method that can be used for the staging of diabetic retinopathy. In addition, alterations in OCTA parameters can precede the clinical fundus changes. In this review, we aimed to assess the accuracy of OCTA in diagnosis and staging of diabetic retinopathy.

METHODS

Two independent reviewers participated in the literature search using electronic databases (PubMed, Embase, Cochrane Library Central Register of Controlled Trials, ISI, and Scopus) from inception till December 2020. The heterogeneity of data was assessed by Q statistics, Chi-square test and I2 index.

RESULTS

Forty-four articles published from 2015 to the end of 2020 were included in this meta-analysis. Of these, 27 were case-control studies, 9 were case series, and 8 were cohort studies. In total, 4284 eyes of 3553 patients were assessed in this study. OCTA could differentiate diabetic retinopathy from diabetes without diabetic retinopathy with a sensitivity of 88% (95% CI: 85% to 92%) and specificity of 88% (95% CI: 85% to 91%). In addition, it could differentiate proliferative diabetic retinopathy from non-proliferative diabetic retinopathy with a sensitivity of 91% (95% CI: 86% to 95%) and specificity of 91% (95% CI:86% to 96%). The sensitivity of OCTA for diagnosing diabetic retinopathy was increased by the size of scan (3 × 3 mm: 85%; 6 × 6 mm: 91%, 12 × 12 mm: 96%).

CONCLUSION

OCTA, as a non-invasive method, has acceptable sensitivity and specificity for diagnosis and classification of diabetic retinopathy. A larger scan size is associated with more sensitivity for discriminating diabetic retinopathy.

Fully automatic segmentation and monitoring of choriocapillaris flow voids in OCTA images

Optical coherence tomography angiography (OCTA) is a non-invasive ophthalmic imaging modality that is widely used in clinical practice. Recent technological advances in OCTA allow imaging of blood flow deeper than the retinal layers, at the level of the choriocapillaris (CC), where a granular image is obtained showing a pattern of bright areas, representing blood flow, and a pattern of small dark regions, called flow voids (FVs). Several clinical studies have reported a close correlation between abnormal FVs distribution and multiple diseases, so quantifying changes in FVs distribution in CC has become an area of interest for many clinicians. However, CC OCTA images present very complex features that make it difficult to correctly compare FVs during the monitoring of a patient. In this work, we propose fully automatic approaches for the segmentation and monitoring of FVs in CC OCTA images. First, a baseline approach, in which a fully automatic segmentation methodology based on local contrast enhancement and global thresholding is proposed to segment FVs and measure changes in their distribution in a straightforward manner. Second, a robust approach in which, prior to the use of our segmentation methodology, an unsupervised trained neural network is used to perform a deformable registration that aligns inconsistencies between images acquired at different time instants. The proposed approaches were tested with CC OCTA images collected during a clinical study on the response to photodynamic therapy in patients affected by chronic central serous chorioretinopathy (CSC), demonstrating their clinical utility. The results showed that both approaches are accurate and robust, surpassing the state of the art, therefore improving the efficacy of FVs as a biomarker to monitor the patient treatments. This gives great potential for the clinical use of our methods, with the possibility of extending their use to other pathologies or treatments associated with this type of imaging.

Retinal Structure Detection in OCTA Image via Voting-Based Multitask Learning

Automated detection of retinal structures, such as retinal vessels (RV), the foveal avascular zone (FAZ), and retinal vascular junctions (RVJ), are of great importance for understanding diseases of the eye and clinical decision-making. In this paper, we propose a novel Voting-based Adaptive Feature Fusion multi-task network (VAFF-Net) for joint segmentation, detection, and classification of RV, FAZ, and RVJ in optical coherence tomography angiography (OCTA). A task-specific voting gate module is proposed to adaptively extract and fuse different features for specific tasks at two levels: features at different spatial positions from a single encoder, and features from multiple encoders. In particular, since the complexity of the microvasculature in OCTA images makes simultaneous precise localization and classification of retinal vascular junctions into bifurcation/crossing a challenging task, we specifically design a task head by combining the heatmap regression and grid classification. We take advantage of three different en face angiograms from various retinal layers, rather than following existing methods that use only a single en face. We carry out extensive experiments on three OCTA datasets acquired using different imaging devices, and the results demonstrate that the proposed method performs on the whole better than either the state-of-the-art single-purpose methods or existing multi-task learning solutions. We also demonstrate that our multi-task learning method generalizes across other imaging modalities, such as color fundus photography, and may potentially be used as a general multi-task learning tool. We also construct three datasets for multiple structure detection, and part of these datasets with the source code and evaluation benchmark have been released for public access.

Detection of Diabetic Retinopathy Using Extracted 3D Features from OCT Images

Diabetic retinopathy (DR) is a major health problem that can lead to vision loss if not treated early. In this study, a three-step system for DR detection utilizing optical coherence tomography (OCT) is presented. First, the proposed system segments the retinal layers from the input OCT images. Second, 3D features are extracted from each retinal layer that include the first-order reflectivity and the 3D thickness of the individual OCT layers. Finally, backpropagation neural networks are used to classify OCT images. Experimental studies on 188 cases confirm the advantages of the proposed system over related methods, achieving an accuracy of 96.81%, using the leave-one-subject-out (LOSO) cross-validation. These outcomes show the potential of the suggested method for DR detection using OCT images.

Machine learning in optical coherence tomography angiography

Optical coherence tomography angiography (OCTA) offers a noninvasive label-free solution for imaging retinal vasculatures at the capillary level resolution. In principle, improved resolution implies a better chance to reveal subtle microvascular distortions associated with eye diseases that are asymptomatic in early stages. However, massive screening requires experienced clinicians to manually examine retinal images, which may result in human error and hinder objective screening. Recently, quantitative OCTA features have been developed to standardize and document retinal vascular changes. The feasibility of using quantitative OCTA features for machine learning classification of different retinopathies has been demonstrated. Deep learning-based applications have also been explored for automatic OCTA image analysis and disease classification. In this article, we summarize recent developments of quantitative OCTA features, machine learning image analysis, and classification.

An effective technique for diabetic retinopathy using hybrid machine learning technique

Diabetic retinopathy (DR) stays as an eye issue that has continuously developed in individuals who experienced diabetes. The complexities in diabetes cause harm to the vein at the back of the retina. In outrageous cases, DR could swift apparition disaster or visual impairment. This genuine impact had the option to charge through convenient treatment and early recognition. As of late, this issue has been spreading quickly, particularly in the working region, which in the end constrained the interest of an analysis of this disease from the most prompt stage. Therefore, that are castoff to protect the progressions of this disorder, revealing of the retinal blood vessels (RBVs) play a foremost role. The growth of an abnormal vessel leads to the development steps of DR, where it can be well known by extracting the RBV. The recognition of the BV for DR by developing an automatic approach is a major aim of our research study. In the proposed method, there are two major steps: one is segmentation and the second one is classification of affected retinal BV. The proposed method uses the Kinetic Gas Molecule Optimization based on centroid initialization used for the Fuzzy C-means Clustering. In the classification step, those segmented images are given as input to hybrid techniques such as a convolution neural network with bidirectional-long short-term memory (CNN with Bi-LSTM). The learning degree of Bi-LSTM is revised by using the self-attention mechanism for refining the classification accuracy. The trial consequences disclosed that the mixture algorithm achieved higher accuracy, specificity, and sensitivity than existing techniques.

An unsupervised hierarchical approach for automatic intra-retinal cyst segmentation in spectral-domain optical coherence tomography images

PURPOSE

Intra-retinal cyst (IRC) is a symptom of macular disorders that occurs due to retinal blood vessel damage and fluid leakage to the macula area. These abnormalities are efficiently visualized using optical coherence tomography (OCT) imaging. These patients need to be regularly monitored for the presence and changes of IRC regions. Thus, automatic segmentation of IRCs can be beneficial to investigate disease progression.

METHODS

In this study, automatic IRC segmentation is accomplished by building three different masks in three unsupervised segmentation levels of a hierarchical framework. In the first level, the ROI-mask (R-mask) is built, and the retina area is cropped based on this mask. In the second level, the prune-mask (P-mask) is built, and the searching space is significantly reduced toward the target objects using this mask; and finally in the third level, by applying the Markov random field (MRF) model and employing intensity and contextual information, the cyst mask (C-mask) is extracted.

RESULTS

The proposed method is evaluated on three datasets including OPTIMA, UMN, and KERMANY datasets. The experimental results showed that the proposed method is effective with a mean dice coefficient rate of 0.74, 0.75 and 0.79 by the intersection of ground truths on the OPTIMA, UMN and KERMANY datasets, respectively.

CONCLUSION

The proposed method outperforms the state-of-the-art methods on the OPTIMA and UMN datasets while achieving comparable results to the most recently proposed method on the KERMANY dataset.

Quantification of Microvascular Density of the Optic Nerve Head in Diabetic Retinopathy Using Optical Coherence Tomographic Angiography

Aims

To quantify the capillary density of the optic nerve head in healthy control eyes and different stages of diabetic retinopathy (DR) eyes and identify the parameters to detect eyes with or without DR using optical coherence tomographic angiography (OCTA).

Methods

In this cross-sectional study, 211 eyes of 121 participants with type 2 diabetes with different stages of DR or without DR and 73 eyes of 38 healthy age-matched controls were imaged by OCTA. Radial peripapillary capillary (RPC) plexus density and retinal nerve fiber layer (RNFL) thickness were examined. The mixed model binary logistic regression model was used to identify the parameters to detect eyes with or without DR. The area under the receiver operating characteristic (ROC) curve was calculated.

Results

RPC density decreased significantly in diabetic patients without DR compared with the healthy controls, and it was negatively correlated with the severity of DR (P < 0.01). RPC density was a significant parameter to distinguish diabetic eyes with or without DR (P < 0.01). The area under the ROC curve was 0.743.

Conclusions

Quantification of RPC density by OCTA provides evidence of microvascular changes in the optic nerve in diabetic patients. RPC density can serve as a possible biomarker in detecting eyes with DR. Larger cohort studies need to support this statement.

Differentiation of Diabetic Status Using Statistical and Machine Learning Techniques on Optical Coherence Tomography Angiography Images

Purpose

To investigate the potential of statistical and machine learning approaches to determine the diabetic status of patients from optical coherence tomography angiography (OCT-A) images.

Methods

This was a retrospective cross-sectional observational study based at Manchester Royal Eye Hospital, United Kingdom. OCT-A scans were sequentially selected from one eye of each of 182 patients who were either not diabetic, diabetic without retinopathy, or diabetic with retinopathy requiring hospital follow-up. Eligible images were analyzed by expert purpose-built automated algorithms to calculate clinically relevant outcome measures. These were used in turn as inputs to machine learning and statistical procedures to derive algorithms to perform clinically relevant classifications of patient images into the clinical groups. Receiver operating characteristic curves for the classifiers were evaluated and predictive accuracy assessed using area under curve (AUC).

Results

For distinguishing diabetic patients from those without diabetes, the Random Forest classifier provided the highest AUC (0.8). For distinguishing diabetic patients with significant retinopathy from those with no retinopathy, the highest AUC was represented by logistic regression (0.91).

Conclusions

The study demonstrates the potential of novel techniques using automated analysis of OCT-A scans to diagnose patients with diabetes, or when diabetic status is known, to automatically determine those that require hospital input.

Translational Relevance

This work advances the concept of a rapid and noninvasive clinical screening tool using OCT-A to determine a patient's diabetic status.

Widefield optical coherence tomography angiography in diabetic retinopathy

AIMS

To evaluate superficial capillary plexus (SCP), deep capillary plexus (DCP) and choriocapillaris (CC) perfusion in macular and near/mid periphery regions in diabetic patients using widefield swept-source optical coherence tomography angiography (WSS-OCTA).

METHODS

Ninety-four diabetic patients (94 eyes) classified as diabetics without diabetic retinopathy (no DR) (25 eyes), mild DR (23 eyes), moderate/severe DR (26 eyes), proliferative DR (20 eyes) and a control group of 25 healthy subjects (25 eyes) were imaged with the WSS-OCTA system (PLEX Elite 9000, Carl Zeiss Meditec Inc., Dublin, CA, USA). Quantitative analysis was performed in the macular and peripheral regions. The main outcome measures were perfusion density (PD) and vessel length density of SCP, DCP and CC.

RESULTS

Peripheral retina (all sectors) showed lower SCP and DCP PD compared to the macular region (p  < 0.001). In diabetics without DR and DR in different stages, SCP and DCP PD significantly decreased at advancing stages of DR (p  < 0.001). At DCP level, central PD was significantly directly related to peripheral PD (superior, R = 0.682 and 0.479; temporal, R  = 0.918 and 0.554; inferior, R  = 0.711). A good sensitivity and an excellent specificity were found in terms of prediction of disease worsening, especially for central and temporal sectors in all plexuses and for all sectors both central and peripheral of DCP.

CONCLUSIONS

The widefield OCTA is useful for the study of central and peripheral retina in diabetic patients with or without diabetic retinopathy, assessing good correlation between central and peripheral retina.

Do microvascular changes occur preceding neural impairment in early-stage diabetic retinopathy? Evidence based on the optic nerve head using optical coherence tomography angiography

AIMS

To evaluate the microvascular and neural differences of the optic nerve head (ONH) between type 2 diabetes mellitus (T2DM) subjects and controls.

METHODS

This was a cross-sectional observational study. One hundred and eight eyes of 108 T2DM subjects with or without diabetic retinopathy (DR) (54 preclinical DR and 54 mild-to-moderate DR) were included. Fifty-two eyes of 52 healthy subjects were included as controls. The 4.5-mm Angio Disc scan mode and the ganglion cell complex scan mode were performed with all participants using AngioVue software 2.0 of the optical coherence tomography angiography (OCTA) device.

RESULTS

Regarding ONH radial peripapillary capillary (RPC) density, the peripapillary region was mainly significantly reduced in the No-DR (NDR) group. Moreover, the RPC density of the peripapillary region and the inside optic disc area were significantly reduced in the non-proliferative DR (NPDR) group. When compared to the controls, significantly reduced peripapillary capillary density in six sections was observed in the NPDR group. However, reduced density was observed in only two sections in the NDR group. The NPDR group had significantly increased focal loss volume (FLV) and reduced peripapillary RNFL thickness in the inferior nasal section compared to those in the controls, but similar changes were not observed in the NDR group. A regression model identified RPCs inside the optic disc as a significant parameter in early-stage DR detection. In the NPDR group, BCVA showed a significantly negative correlation with RPCs inside the optic disc and a significantly positive correlation with FLV.

CONCLUSIONS

OCTA findings of the ONH area may provide evidence that microvascular changes occur preceding neural impairment in early-stage DR. However, further researches are still needed to support the statement. Reduced ONH perfusion inside the optic disc may be one of the crucial biomarkers in early-stage DR detection and is a possible sensitive visual acuity predictor in early-stage DR subjects. With the ONH mode, OCTA may be a more promising tool in DR screening.

OCT Angiography: A Technique for the Assessment of Retinal and Optic Nerve Diseases in the Pediatric Population

Optical coherence tomography angiography (OCT-A) is a novel, rapidly evolving, non-invasive imaging technique that allows images of the retinal vasculature to be obtained in a few seconds. Blood vessels of different retinal vascular plexuses and the foveal avascular zone (FAZ) can be examined without the administration of any contrast or dye. Due to these characteristics, OCT-A could be an excellent complementary test to study retinal vascularization in children. Until now, most of the studies with OCT-A have been conducted in adults and only a few have been carried out in children. In this review, we describe the principles and advantages of OCT-A over traditional imaging methods and provide a summary of the OCT-A findings in retinopathy of prematurity and other retinal and optic disc pathologies in children. In view of the promising results from studies, the advantages of a relatively rapid and non-invasive method to assess the retinal vasculature makes OCT-A a tool of which applications in the field of pediatric ophthalmology will be expanded in the near future for patient diagnosis and follow-up in every day clinical practice.