Automated Quantification of Nonperfusion in Three Retinal Plexuses Using Projection-Resolved Optical Coherence Tomography Angiography in Diabetic Retinopathy

Volumetric Measures of Capillary Nonperfusion on Optical Coherence Tomography Angiography Detect Early Ischemia in Diabetes Without Retinopathy

Purpose

The purpose of this study was to compare volumetric 3-dimensional (3D) against standard 2-dimensional (2D) measurements of ischemia for distinguishing early stages of diabetic retinopathy (DR) using optical coherence tomography angiography (OCTA).

Methods

This cross-sectional study considered 82 eyes of 82 patients (aged 51.0 ± 11.9 years) including 27 healthy controls, 31 patients with diabetes mellitus (DM) without DR, and 24 patients with mild nonproliferative DR (NPDR). Using OCTA, we obtained 2D scans and 3D volumes of the superficial capillary plexus (SCP), middle capillary plexus (MCP), and deep capillary plexus (DCP). We calculated geometric perfusion deficits (GPDs), which define ischemic regions as those located farther than a specified threshold distance from the nearest blood vessel. For the GPD parameter, we compared the performance of a 20 µm versus 30 µm cutoff.

Results

On 2D scans, eyes with mild NPDR had significantly higher GPDs in all 3 retinal capillary layers, indicating worse ischemia, compared with both healthy controls and patients with DM without DR, using either threshold (20 µm or 30 µm) to define GPD (all P < 0.05). DM without DR showed no significant difference from healthy eyes in 2D images. Interestingly, however, using 3D volumes, DM without DR eyes had significantly greater DCP GPDs than healthy eyes using a GPD threshold of 20 µm (P = 0.012), but not with 30 µm (P = 0.057).

Conclusions

Using a stringent threshold (20 µm), volumetric OCTA imaging detects significant DCP perfusion defects in diabetic eyes even before DR onset, whereas traditional 2D OCTA does not. Volumetric scans may therefore be more sensitive to early ischemia in diabetes.

Artificial Intelligence Versus Rules-Based Approach for Segmenting NonPerfusion Area in a DRCR Retina Network Optical Coherence Tomography Angiography Dataset

Purpose

Loss of retinal perfusion is associated with both onset and worsening of diabetic retinopathy (DR). Optical coherence tomography angiography is a noninvasive method for measuring the nonperfusion area (NPA) and has promise as a scalable screening tool. This study compares two optical coherence tomography angiography algorithms for quantifying NPA.

Methods

Adults with (N = 101) and without (N = 274) DR were recruited from 20 U.S. sites. We collected 3 × 3-mm macular scans using an Optovue RTVue-XR. Rules-based (RB) and deep-learning-based artificial intelligence (AI) algorithms were used to segment the NPA into four anatomical slabs. For comparison, a subset of scans (n = 50) NPA was graded manually.

Results

The AI method outperformed the RB method in intersection over union, recall, and F1 score, but the RB method has better precision relative to manual grading in all anatomical slabs (all P ≤ 0.001). The AI method had a stronger rank correlation with Early Treatment of Diabetic Retinopathy Study DR severity than the RB method in all slabs (all P < 0.001). NPAs graded using the AI method had a greater area under the receiver operating characteristic curve for diagnosing referable DR than the RB method in the superficial vascular complex, intermediate capillary plexus, and combined inner retina (all P ≤ 0.001), but not in the deep capillary plexus (P = 0.92).

Conclusions

Our results indicate that output from the AI-based method agrees better with manual grading and can better distinguish between clinically relevant DR severity levels than a RB approach using most plexuses.

Quantitative Volumetric Analysis of Retinal Ischemia with an Oxygen Diffusion Model and OCT Angiography

Purpose

Retinal ischemia is a major feature of diabetic retinopathy (DR). Traditional nonperfused areas measured by OCT angiography (OCTA) measure blood supply but not ischemia. We propose a novel 3-dimensional (3D) quantitative method to derive ischemia measurements from OCTA data.

Design

Cross-sectional study.

Participants

We acquired 223 macular OCTA volumes from 33 healthy eyes, 33 diabetic eyes without retinopathy, 7 eyes with nonreferable DR, 17 eyes with referable but nonvision-threatening DR, and 133 eyes with vision-threatening DR.

Methods

Each eye was scanned using a spectral-domain OCTA system (Avanti RTVue-XR, Visionix/Optovue, Inc) with 1.6-mm scan depth in a 3 × 3-mm region (640 × 304 × 304 voxels) centered on the fovea. For each scanned OCTA volume, a custom algorithm removed flow projection artifacts. We then enhanced, binarized, and skeletonized the vasculature in each OCTA volume and generated a 3D oxygen tension map using a zero-order kinetics oxygen diffusion model. Each volume was scaled to the average retina thickness in healthy controls after foveal registration and flattening of the Bruch's membrane. Finally, we extracted 3D ischemia maps by comparison with a reference map established from scans of healthy eyes using the same processing. To assess the ability of the ischemia maps to grade DR severity, we constructed receiver operating characteristic curves for diagnosing diabetes, referable DR, and vision-threatening DR.

Main Outcome Measures

Spearman correlation coefficient and area under receiver operating characteristic curve (AUC) were used to quantify the ability of the ischemia maps to DR.

Results

The ischemia maps showed that the ischemic tissues were at or near pathologically nonperfused areas, but not the normally nonvascular tissue, such as the foveal avascular zone. We found multiple novel metrics, including inferred 3D-oxygen tension, ischemia index, and ischemic volume ratio, were strongly correlated with DR severity. The AUCs of ischemia index measured were 0.94 for diabetes, 0.89 for DR, 0.88 for referable DR, and 0.85 for vision-threatening DR.

Conclusions

A quantitative method to infer 3D oxygen tension and ischemia using OCTA in diabetic eyes can identify ischemic tissue that are more specific to pathologic changes in DR.

Financial Disclosures

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Perfusion Deficits in Diabetes Without Retinopathy Localize to the Perivenular Deep Capillaries Near the Fovea on OCT Angiography

Purpose

To localize early capillary perfusion deficits in patients with diabetes mellitus (DM) without clinical diabetic retinopathy (DR) using averaged OCT angiography (OCTA).

Design

Retrospective cross-sectional study.

Participants

Patients with DM without DR and healthy controls.

Methods

We measured perfusion deficits in the full retina, superficial capillary plexus (SCP), and deep capillary plexus (DCP) on averaged 3 × 3-mm OCTA images. Perfusion deficits were defined as the percentage of retinal tissue located >30 μm from blood vessels, excluding the foveal avascular zone (FAZ). One eye from each patient was selected based on image quality. We measured deficits in the parafoveal region, the 300 μm surrounding the FAZ, and 300 to 1000 μm surrounding the FAZ. If a capillary layer within one of these regions was significantly different in DM without DR compared with controls, we further characterized the location of perfusion deficit as periarteriolar, perivenular, or the capillaries between these 2 zones.

Main Outcome Measures

Location of increased perfusion deficits in patients with DM without DR compared with controls.

Results

Sixteen eyes from 16 healthy controls were compared with 16 eyes from 16 patients with DM without DR (age 45.1 ± 10.7 and 47.4 ± 15.2 years respectively, P = 0.64). Foveal avascular zone area and perfusion deficits in the entire parafovea and the 300 to 1000-μm ring around the FAZ were not significantly different between groups (P > 0.05 for all). Perfusion deficits in 300 μm around the FAZ were significantly increased in patients with DM without DR in full retinal thickness, SCP, and DCP (P < 0.05 for all). When analyzing the perivenular, periarteriolar, and capillary zones, only the perivenular DCP perfusion deficits were significantly increased (5.03 ± 2.92% in DM without DR and 2.73 ± 1.97% in controls, P = 0.014).

Conclusions

Macular perfusion deficits in patients with DM without DR were significantly increased in the region nearest the FAZ, mainly at the perivenular deep capillaries. Further research on these early changes may improve our understanding of the capillaries most susceptible to vascular injury and disruption during diabetes.

Financial Disclosures

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Optical coherence tomography angiography in diabetic retinopathy: A major review

Diabetic retinopathy (DR) is characterized by retinal vasculopathy and is a leading cause of visual impairment. Optical coherence tomography angiography (OCTA) is an innovative imaging technology that can detect various pathologies and quantifiable changes in retinal microvasculature. We briefly describe its functional principles and advantages over fluorescein angiography and perform a comprehensive review on its clinical applications in the screening or management of people with prediabetes, diabetes without clinical retinopathy (NDR), nonproliferative DR (NPDR), proliferative DR (PDR), and diabetic macular edema (DME). OCTA reveals early microvascular alterations in prediabetic and NDR eyes, which may coexist with sub-clinical neuroretinal dysfunction. Its applications in NPDR include measuring ischemia, detecting retinal neovascularization, and timing of early treatment through predicting the risk of retinopathy worsening or development of DME. In PDR, OCTA helps characterize the flow within neovascular complexes and evaluate their progression or regression in response to treatment. In eyes with DME, OCTA perfusion parameters may be of predictive value regarding the visual and anatomical gains associated with treatment. We further discussed the limitations of OCTA and the benefits of its incorporation into an updated DR severity scale.

Multi-Plexus Nonperfusion Area Segmentation in Widefield OCT Angiography Using a Deep Convolutional Neural Network

Purpose

To train and validate a convolutional neural network to segment nonperfusion areas (NPAs) in multiple retinal vascular plexuses on widefield optical coherence tomography angiography (OCTA).

Methods

This cross-sectional study included 202 participants with a full range of diabetic retinopathy (DR) severities (diabetes mellitus without retinopathy, mild to moderate non-proliferative DR, severe non-proliferative DR, and proliferative DR) and 39 healthy participants. Consecutive 6 × 6-mm OCTA scans at the central macula, optic disc, and temporal region in one eye from 202 participants in a clinical DR study were acquired with a 70-kHz OCT commercial system (RTVue-XR). Widefield OCTA en face images were generated by montaging the scans from these three regions. A projection-resolved OCTA algorithm was applied to remove projection artifacts at the voxel scale. A deep convolutional neural network with a parallel U-Net module was designed to detect NPAs and distinguish signal reduction artifacts from flow deficits in the superficial vascular complex (SVC), intermediate capillary plexus (ICP), and deep capillary plexus (DCP). Expert graders manually labeled NPAs and signal reduction artifacts for the ground truth. Sixfold cross-validation was used to evaluate the proposed algorithm on the entire dataset.

Results

The proposed algorithm showed high agreement with the manually delineated ground truth for NPA detection in three retinal vascular plexuses on widefield OCTA (mean ± SD F-score: SVC, 0.84 ± 0.05; ICP, 0.87 ± 0.04; DCP, 0.83 ± 0.07). The extrafoveal avascular area in the DCP showed the best sensitivity for differentiating eyes with diabetes but no retinopathy (77%) from healthy controls and for differentiating DR by severity: DR versus no DR, 77%; referable DR (rDR) versus non-referable DR (nrDR), 79%; vision-threatening DR (vtDR) versus non-vision-threatening DR (nvtDR), 60%. The DCP also showed the best area under the receiver operating characteristic curve for distinguishing diabetes from healthy controls (96%), DR versus no DR (95%), and rDR versus nrDR (96%). The three-plexus-combined OCTA achieved the best result in differentiating vtDR and nvtDR (81.0%).

Conclusions

A deep learning network can accurately segment NPAs in individual retinal vascular plexuses and improve DR diagnostic accuracy.

Translational Relevance

Using a deep learning method to segment nonperfusion areas in widefield OCTA can potentially improve the diagnostic accuracy of diabetic retinopathy by OCT/OCTA systems.

An OCT-A Analysis of the Importance of Intermediate Capillary Plexus in Diabetic Retinopathy: A Brief Review

Optical coherence tomography-angiography is a technique that allows us to non-invasively study in vivo the different retinal vascular networks. This allows a deeper understanding of retinal capillary anatomy and function, in addition to the pathophysiologic changes encountered in diverse diseases. The four retinal capillary layers have different anatomies and functions, implying distinct adaptation and roles in the course of the diseases. Diabetic retinopathy is the leading cause of blindness in working-age adults. Several studies have evaluated how each retinal capillary layer is specifically affected according to the stage of the disease. Unfortunately, too few studies have considered the intermediate capillary plexus as a separate layer, as it has often been incorporated in another layer. In this review, we shed light on the potential role the intermediate capillary plexus plays in the physiopathology of diabetic retinal disease as well as its potential use in grading diabetic retinopathy and its clinical added value in estimating the disease prognosis.

Visualizing features with wide-field volumetric OCT angiography

Optical coherence tomography (OCT) and its extension OCT angiography (OCTA) have become essential clinical imaging modalities due to their ability to provide depth-resolved angiographic and tissue structural information non-invasively and at high resolution. Within a field of view, the anatomic detail available is sufficient to identify several structural and vascular pathologies that are clinically relevant for multiple prevalent blinding diseases, including age-related macular degeneration (AMD), diabetic retinopathy (DR), and vein occlusions. The main limitation in contemporary OCT devices is that this field of view is limited due to a fundamental trade-off between system resolution/sensitivity, sampling density, and imaging window dimensions. Here, we describe a swept-source OCT device that can capture up to a 12 × 23-mm field of view in a single shot and show that it can identify conventional pathologic features such as non-perfusion areas outside of conventional fields of view. We also show that our approach maintains sensitivity sufficient to visualize novel features, including choriocapillaris morphology beneath the macula and macrophage-like cells at the inner limiting membrane, both of which may have implications for disease.

Interpretable Diabetic Retinopathy Diagnosis Based on Biomarker Activation Map

OBJECTIVE

Deep learning classifiers provide the most accurate means of automatically diagnosing diabetic retinopathy (DR) based on optical coherence tomography (OCT) and its angiography (OCTA). The power of these models is attributable in part to the inclusion of hidden layers that provide the complexity required to achieve a desired task. However, hidden layers also render algorithm outputs difficult to interpret. Here we introduce a novel biomarker activation map (BAM) framework based on generative adversarial learning that allows clinicians to verify and understand classifiers' decision-making.

METHODS

A data set including 456 macular scans were graded as non-referable or referable DR based on current clinical standards. A DR classifier that was used to evaluate our BAM was first trained based on this data set. The BAM generation framework was designed by combing two U-shaped generators to provide meaningful interpretability to this classifier. The main generator was trained to take referable scans as input and produce an output that would be classified by the classifier as non-referable. The BAM is then constructed as the difference image between the output and input of the main generator. To ensure that the BAM only highlights classifier-utilized biomarkers an assistant generator was trained to do the opposite, producing scans that would be classified as referable by the classifier from non-referable scans.

RESULTS

The generated BAMs highlighted known pathologic features including nonperfusion area and retinal fluid.

CONCLUSION/SIGNIFICANCE

A fully interpretable classifier based on these highlights could help clinicians better utilize and verify automated DR diagnosis.

OCT-Angiography of deep and superficial retinal vascular density changes in diabetes without diabetic retinopathy

PURPOSE

To analyze the vascular density values (VD) in the superficial and the deep retinal network and in all perimacular sectors of diabetic eyes without clinical retinopathy, comparing them to that of healthy, nondiabetic control eyes. We investigated the factors that may influence these values.

METHODS

This was a cross-sectional study including diabetics without diabetic retinopathy and healthy control subjects. All subjects underwent OCTA examination (RTVue-XR Avanti; Optovue, Fremont, CA, USA). The scanned area was 3×3mm and centered on the fovea. In the superficial and deep capillary plexus, we evaluated the total VD, parafoveal, temporal, nasal, superior and inferior sectors, as well as the superior and inferior hemi-sector VD. The sectors with the greatest area under the curve (AUC) were determined. We evaluated the correlation between certain risk factors and VD values.

RESULTS

VD values in all sectors of the superficial capillary plexus layer and deep capillary plexus layer in the diabetic group were significantly lower than in the control group. Among all the parameters, the superficial total VD and superficial parafoveal VD had the greatest AUC (0.992 and 0.991 respectively). The sector with the greatest AUC was the temporal sector in both the SCP (0.990) and DCP (0.976). Age, creatinine clearance and hyperlipemia correlated with vascular density.

CONCLUSIONS

Superficial and deep retinal VD are both decreased in diabetic patients without diabetic retinopathy. Our results suggest that OCTA might be a promising tool for diabetic retinopathy screening. Quantitative microvascular changes might precede clinical damage.

Association between macrophage-like cell density and ischemia metrics in diabetic eyes

We previously showed that macrophage-like cells (MLCs) are increased in eyes with advanced diabetic retinopathy (DR). Here, we hypothesized that MLC density was correlated with ischemia using optical coherence tomography angiography (OCTA) and ultra-widefield fluorescein angiography (UWF-FA). Treatment-naïve diabetic eyes were prospectively imaged with repeated OCTA (average 5.3 scans per eye) and UWF-FA imaging. OCTA images were registered and averaged to generate a superficial capillary plexus (SCP), deep capillary plexus (DCP), and MLC slab. We calculated geometric perfusion deficit (GPD), vessel length density, and vessel density for the SCP and DCP. MLC density was quantified by two masked graders and averaged. Ischemia on UWF-FA was measured to generate a non-perfusion area (NPA) and index (NPI). Since MLC density was non-parametrically distributed, MLC density was correlated with ischemia metrics using Spearman correlations. Forty-five treatment-naïve eyes of 45 patients (59 ± 12 years of age; 56% female) were imaged. We included 6 eyes with no DR, 7 eyes with mild non-proliferative DR (NPDR), 22 moderate NPDR, 4 severe NPDR, and 6 PDR eyes. MLC density between graders was highly correlated (r = 0.9592, p < 0.0001). MLC density was correlated with DCP GPD (r = 0.296, p = 0.049), but no other OCTA ischemia metrics. MLC density was also correlated with UWF-FA NPA (r = 0.330, p = 0.035) and NPI (r = 0.332, p = 0.034). MLC density was correlated with total ischemia on UWF-FA and local DCP GPD. Since both UWF-FA and DCP non-perfusion are associated with higher risk for DR progression, MLC density could be another potential biomarker for DR progression.

Automated segmentation of ultra-widefield fluorescein angiography of diabetic retinopathy using deep learning

BACKGROUND/AIMS

Retinal capillary non-perfusion (NP) and neovascularisation (NV) are two of the most important angiographic changes in diabetic retinopathy (DR). This study investigated the feasibility of using deep learning (DL) models to automatically segment NP and NV on ultra-widefield fluorescein angiography (UWFA) images from patients with DR.

METHODS

Retrospective cross-sectional chart review study. In total, 951 UWFA images were collected from patients with severe non-proliferative DR (NPDR) or proliferative DR (PDR). Each image was segmented and labelled for NP, NV, disc, background and outside areas. Using the labelled images, DL models were trained and validated (80%) using convolutional neural networks (CNNs) for automated segmentation and tested (20%) on test sets. Accuracy of each model and each label were assessed.

RESULTS

The best accuracy from CNN models for each label was 0.8208, 0.8338, 0.9801, 0.9253 and 0.9766 for NP, NV, disc, background and outside areas, respectively. The best Intersection over Union for each label was 0.6806, 0.5675, 0.7107, 0.8551 and 0.924 and mean mean boundary F1 score (BF score) was 0.6702, 0.8742, 0.9092, 0.8103 and 0.9006, respectively.

CONCLUSIONS

DL models can detect NV and NP as well as disc and outer margins on UWFA with good performance. This automated segmentation of important UWFA features will aid physicians in DR clinics and in overcoming grader subjectivity.

Optical coherence tomography angiography in diabetic retinopathy

There remain many unanswered questions on how to assess and treat the pathology and complications that arise from diabetic retinopathy (DR). Optical coherence tomography angiography (OCTA) is a novel and non-invasive three-dimensional imaging method that can visualize capillaries in all retinal layers. Numerous studies have confirmed that OCTA can identify early evidence of microvascular changes and provide quantitative assessment of the extent of diseases such as DR and its complications. A number of informative OCTA metrics could be used to assess DR in clinical trials, including measurements of the foveal avascular zone (FAZ; area, acircularity, 3D para-FAZ vessel density), vessel density, extrafoveal avascular zones, and neovascularization. Assessing patients with DR using a full-retinal slab OCTA image can limit segmentation errors and confounding factors such as those related to center-involved diabetic macular edema. Given emerging data suggesting the importance of the peripheral retinal vasculature in assessing and predicting DR progression, wide-field OCTA imaging should also be used. Finally, the use of automated methods and algorithms for OCTA image analysis, such as those that can distinguish between areas of true and false signals, reconstruct images, and produce quantitative metrics, such as FAZ area, will greatly improve the efficiency and standardization of results between studies. Most importantly, clinical trial protocols should account for the relatively high frequency of poor-quality data related to sub-optimal imaging conditions in DR and should incorporate time for assessing OCTA image quality and re-imaging patients where necessary.

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.

OCT angiography and its retinal biomarkers [Invited]

Optical coherence tomography angiography (OCTA) is a high-resolution, depth-resolved imaging modality with important applications in ophthalmic practice. An extension of structural OCT, OCTA enables non-invasive, high-contrast imaging of retinal and choroidal vasculature that are amenable to quantification. As such, OCTA offers the capability to identify and characterize biomarkers important for clinical practice and therapeutic research. Here, we review new methods for analyzing biomarkers and discuss new insights provided by OCTA.

Macular Perfusion Deficits on OCT Angiography Correlate with Nonperfusion on Ultrawide-field Fluorescein Angiography in Diabetic Retinopathy

OBJECTIVE

To evaluate the correlation between nonperfusion parameters on OCT angiography (OCTA) and ultrawide-field fluorescein angiography (UWF-FA) in subjects with diabetes mellitus (DM).

DESIGN

Prospective, cross-sectional study.

SUBJECTS

Subjects with DM and a wide range of diabetic retinopathy (DR) severity seen at a tertiary referral center.

METHODS

We used averaged 3 × 3 mm OCTA scans to measure geometric perfusion deficit (GPD), vessel density, and vessel length density in the full retina, superficial capillary plexuses (SCPs), and deep capillary plexuses (DCPs). Nonperfusion was manually delineated on UWF-FA to quantify central, peripheral, and total retinal nonperfusion (mm2 and % area).

MAIN OUTCOME MEASURES

Correlation between OCTA parameters and UWF-FA nonperfusion, and accuracy of these OCTA and UWF-FA parameters in detecting clinically referable eyes, using receiver operating characteristic (ROC) curve analysis, sensitivity, specificity, and area under the ROC curve (AUC).

RESULTS

The study included 67 eyes (12 eyes with no signs of DR, 8 mild, 22 moderate, 14 severe nonproliferative DR, and 11 treatment-naive proliferative DR). There was a fair-to-moderate correlation between either central or total retinal nonperfusion on UWF-FA (mm2) and GPD in the SCP (r = 0.482 and r = 0.464, respectively) and DCP (r = 0.470 and r = 0.456, respectively). Receiver operating characteristic analysis showed the DCP GPD significantly superior to other OCTA parameters at the DCP with the largest overall AUC on OCTA for distinguishing referable DR (0.905). Furthermore, the GPD parameter had the largest AUC in each respective capillary layer compared with other parameters. Overall, the total UWF-FA nonperfusion area showed a comparable AUC (0.907) and performed significantly better than peripheral nonperfusion (P = 0.041). Comparing the AUC values between GPD and UWF-FA nonperfusion parameters showed no significant difference in discerning referable DR.

CONCLUSIONS

Nonperfusion as quantified on OCTA (3 × 3 mm) correlated with UWF-FA parameters and both were comparable in detecting referable DR. These macular OCTA metrics, particularly DCP GPD, have the potential for gauging the overall ischemic status of the retina, with an important clinical role in identifying eyes with clinically referable DR.

FINANCIAL DISCLOSURE(S)

Proprietary or commercial disclosure may be found after the references.

Retinal and choroidal microvascular characterization and density changes in different stages of diabetic retinopathy eyes

Background

The purpose of this study was to evaluate the changes in fundus vascular density and micromorphological structure of all vascular plexuses during the different stages of diabetic retinopathy (DR), and the correlation between fundus blood flow and the DR severity.

Methods

This observational cross-sectional study was conducted of 50 eyes with different stages of DR, 25 diabetes mellitus (DM) patients without clinical signs of DR and 41 healthy eyes. The foveal avascular zone (FAZ), vessel density of superficial capillary plexus (SCP), and deep retinal capillary plexus (DCP) were acquired by RTVue XR Avanti OCTA device. The perfusion density (PD), skeleton vessel density (SVD) was manually calculated using ImageJ. The area under receiver operating characteristic (ROC) curve was used to determine the diagnostic value of OCTA parameters in distinguishing DR and healthy eyes.

Results

The choroidal VD were significantly higher in the healthy group than in the DM without DR, NPDR, and PDR groups (p < 0.001). The mean retinal parafovea VD, PD, and retinal SVD were higher in healthy and DM without DR eyes compared with NPDR and DR eyes in all vascular layers (p < 0.001). The parafoveal VD of SCP, and DCP decreased, and FAZ area increased with the exacerbation of DR. The OCTA parameters, including FAZ area, parafovea VD, PD, and SVD in all vascular layers showed significant correlation with DR severity (all p < 0.001). ROC curves of OCTA parameters (FAZ area, retinal parafovea VD, retinal PD, and SVD in all vascular layers) for had high sensitivity and specificity in distinguishing DR versus healthy eyes.

Conclusion

The choroidal parafovea VD, retinal parafovea VD, retinal PD, and SVD in the two plexuses decreased, and retinal FAZ area increased significantly with worsening DR. VD, PD, and SVD might be potential early biomarkers indicating the progression of DR before appearance of clinically PDR in patients with DM. In this study, OCTA parameters had high sensitivity and specificity in distinguishing DR and healthy eyes.

Signal attenuation-compensated projection-resolved OCT angiography

Projection artifacts are a significant limitation of optical coherence tomographic angiography (OCTA). Existing techniques to suppress these artifacts are sensitive to image quality, becoming less reliable on low-quality images. In this study, we propose a novel signal attenuation-compensated projection-resolved OCTA (sacPR-OCTA) algorithm. In addition to removing projection artifacts, our method compensates for shadows beneath large vessels. The proposed sacPR-OCTA algorithm improves vascular continuity, reduces the similarity of vascular patterns in different plexuses, and removes more residual artifacts compared to existing methods. In addition, the sacPR-OCTA algorithm better preserves flow signal in choroidal neovascular lesions and shadow-affected areas. Because sacPR-OCTA processes the data along normalized A-lines, it provides a general solution for removing projection artifacts agnostic to the platform.

Normalized Blood Flow Index in Optical Coherence Tomography Angiography Provides a Sensitive Biomarker of Early Diabetic Retinopathy

Purpose

To evaluate the sensitivity of normalized blood flow index (NBFI) for detecting early diabetic retinopathy (DR).

Methods

Optical coherence tomography angiography (OCTA) images of healthy controls, diabetic patients without DR (NoDR), and patients with mild nonproliferative DR (NPDR) were analyzed in this study. The OCTA images were centered on the fovea and covered a 6 mm × 6 mm area. Enface projections of the superficial vascular plexus (SVP) and the deep capillary plexus (DCP) were obtained for the quantitative OCTA feature analysis. Three quantitative OCTA features were examined: blood vessel density (BVD), blood flow flux (BFF), and NBFI. Each feature was calculated from both the SVP and DCP and their sensitivities to distinguish the three cohorts of the study were evaluated.

Results

The only quantitative feature capable of distinguishing all three cohorts was NBFI in the DCP image. Comparative study revealed that both BVD and BFF were able to distinguish the controls and NoDR from mild NPDR. However, neither BVD nor BFF was sensitive enough to separate NoDR from the healthy controls.

Conclusions

The NBFI has been demonstrated as a sensitive biomarker of early DR, revealing retinal blood flow abnormality better than traditional BVD and BFF. The NBFI in the DCP was verified as the most sensitive biomarker, supporting that diabetes affects the DCP earlier than SVP in DR.

Translational Relevance

NBFI provides a robust biomarker for quantitative analysis of DR-caused blood flow abnormalities, promising early detection and objective classification of DR.

Deep Learning with a Dataset Created Using Kanno Saitama Macro, a Self-Made Automatic Foveal Avascular Zone Extraction Program

The extraction of the foveal avascular zone (FAZ) from optical coherence tomography angiography (OCTA) images has been used in many studies in recent years due to its association with various ophthalmic diseases. In this study, we investigated the utility of a dataset for deep learning created using Kanno Saitama Macro (KSM), a program that automatically extracts the FAZ using swept-source OCTA. The test data included 40 eyes of 20 healthy volunteers. For training and validation, we used 257 eyes from 257 patients. The FAZ of the retinal surface image was extracted using KSM, and a dataset for FAZ extraction was created. Based on that dataset, we conducted a training test using a typical U-Net. Two examiners manually extracted the FAZ of the test data, and the results were used as gold standards to compare the Jaccard coefficients between examiners, and between each examiner and the U-Net. The Jaccard coefficient was 0.931 between examiner 1 and examiner 2, 0.951 between examiner 1 and the U-Net, and 0.933 between examiner 2 and the U-Net. The Jaccard coefficients were significantly better between examiner 1 and the U-Net than between examiner 1 and examiner 2 (p < 0.001). These data indicated that the dataset generated by KSM was as good as, if not better than, the agreement between examiners using the manual method. KSM may contribute to reducing the burden of annotation in deep learning.

The Association Between Regional Macula Vessel Density and Central Visual Field Damage in Advanced Glaucoma Eyes

PRCIS

Both macular superficial vessel density and ganglion cell complex (GCC) thickness measurement are significantly associated with regional and global 10-degree central visual field (VF) sensitivity in advanced glaucoma.

PURPOSE

The purpose of this study was to evaluate the regional and global structure-function relationships between macular vessel density (MVD) assessed by optical coherence tomography angiography (OCTA) and 10-2 VF sensitivity in advanced open angle glaucoma eyes.

METHODS

Macular OCTA and 10-2 VF sensitivity of 44 patients [mean deviation (MD) <-10 dB] were evaluated. Regional and global VF mean sensitivity (MS) was calculated from total deviation plots. Superficial and deep MVD were obtained from 3 × 3 and 6×6 mm 2 OCTA scans using 2 sectoral definitions. Spectral-domain optical coherence tomography macular GCC thickness was obtained simultaneously from the same scan as the MVD measurements. Linear regression models were used to assess the associations ( R2 ).

RESULTS

Lower MS was significantly associated with a reduction in superficial MVD and GCC in each region of both scan sizes for both maps. Associations were weaker in the individual sectors of the whole image grid than the Early Treatment Diabetic Retinopathy Study map. Deep-layer MVD was not associated with central MS. Although 6×6 mm 2 and perifoveal vessel density had better associations with central 10-degree MS compared with GCC thickness (eg, R2 from 25.7 to 48.1 µm and 7.8% to 32.5%, respectively), GCC associations were stronger than MVD associations in the central 5-degree MS.

CONCLUSIONS

Given a stronger MVD-central 10-degree VF association compared with GCC, as well as stronger GCC-central 5-degree VF association compared with MVD, MVD and GCC are complementary measurements in eyes with advanced glaucoma. A longitudinal analysis is needed to determine the relative utility of the GCC and MVD measurements.

A Diabetic Retinopathy Classification Framework Based on Deep-Learning Analysis of OCT Angiography

Purpose

Reliable classification of referable and vision threatening diabetic retinopathy (DR) is essential for patients with diabetes to prevent blindness. Optical coherence tomography (OCT) and its angiography (OCTA) have several advantages over fundus photographs. We evaluated a deep-learning-aided DR classification framework using volumetric OCT and OCTA.

Methods

Four hundred fifty-six OCT and OCTA volumes were scanned from eyes of 50 healthy participants and 305 patients with diabetes. Retina specialists labeled the eyes as non-referable (nrDR), referable (rDR), or vision threatening DR (vtDR). Each eye underwent a 3 × 3-mm scan using a commercial 70 kHz spectral-domain OCT system. We developed a DR classification framework and trained it using volumetric OCT and OCTA to classify eyes into rDR and vtDR. For the scans identified as rDR or vtDR, 3D class activation maps were generated to highlight the subregions which were considered important by the framework for DR classification.

Results

For rDR classification, the framework achieved a 0.96 ± 0.01 area under the receiver operating characteristic curve (AUC) and 0.83 ± 0.04 quadratic-weighted kappa. For vtDR classification, the framework achieved a 0.92 ± 0.02 AUC and 0.73 ± 0.04 quadratic-weighted kappa. In addition, the multiple DR classification (non-rDR, rDR but non-vtDR, or vtDR) achieved a 0.83 ± 0.03 quadratic-weighted kappa.

Conclusions

A deep learning framework only based on OCT and OCTA can provide specialist-level DR classification using only a single imaging modality.

Translational Relevance

The proposed framework can be used to develop clinically valuable automated DR diagnosis system because of the specialist-level performance showed in this study.

Diabetic macular ischaemia- a new therapeutic target?

Diabetic macular ischaemia (DMI) is traditionally defined and graded based on the angiographic evidence of an enlarged and irregular foveal avascular zone. However, these anatomical changes are not surrogate markers for visual impairment. We postulate that there are vascular phenotypes of DMI based on the relative perfusion deficits of various retinal capillary plexuses and choriocapillaris. This review highlights several mechanistic pathways, including the role of hypoxia and the complex relation between neurons, glia, and microvasculature. The current animal models are reviewed, with shortcomings noted. Therefore, utilising the advancing technology of optical coherence tomography angiography (OCTA) to identify the reversible DMI phenotypes may be the key to successful therapeutic interventions for DMI. However, there is a need to standardise the nomenclature of OCTA perfusion status. Visual acuity is not an ideal endpoint for DMI clinical trials. New trial endpoints that represent disease progression need to be developed before irreversible vision loss in patients with DMI. Natural history studies are required to determine the course of each vascular and neuronal parameter to define the DMI phenotypes. These DMI phenotypes may also partly explain the development and recurrence of diabetic macular oedema. It is also currently unclear where and how DMI fits into the diabetic retinopathy severity scales, further highlighting the need to better define the progression of diabetic retinopathy and DMI based on both multimodal imaging and visual function. Finally, we discuss a complete set of proposed therapeutic pathways for DMI, including cell-based therapies that may provide restorative potential.

Quantitative Evaluation of Retinal Microvascular Abnormalities in Patients With Type 2 Diabetes Mellitus Without Clinical Sign of Diabetic Retinopathy

Purpose

To evaluate microvascular abnormalities in the macula and peripapillary area in diabetic patients without clinical signs of diabetic retinopathy (DR) and compare them with healthy control eyes, using optical coherence tomography angiography (OCTA).

Methods

A prospective study was performed of 49 eyes from 49 diabetic patients without clinical signs of DR and a control group of 52 eyes from 52 healthy normal individuals. The 3 × 3 mm macular scans and 4.5 × 4.5 mm optic disc scans were obtained with the OCTA RTVue-XR Avanti system. Angiograms from the superficial capillary plexus, the deep capillary plexus of the macula scans, and radial peripapillary capillary plexus of the optic disc scans were analyzed with MATLAB. Multivariate binary logistic regression and the least absolute shrinkage and selection operator (LASSO) regression were used to select ideal parameters that distinguish diabetic eyes without DR from normal eyes. A receiver operating characteristic (ROC) curve was generated, and sensitivity and specificity were calculated.

Results

Our final model identified FD-300 (foveal vessel density in a 300-µm-wide region around foveal avascular zone) as the only parameter selected by both the LASSO regression and the final multivariate logistic regression model that significantly differentiates diabetic eyes without clinical signs of DR from healthy normal eyes. The area under the ROC curve of FD-300 was 0.685, and sensitivity and specificity were 65.3% and 71.2%, respectively.

Conclusions

Quantitative evaluation of retinal microvascular abnormalities using OCTA identified FD-300 as a useful biomarker versus the other macular and peripapillary OCTA metrics in the early detection of preclinical diabetic retinal abnormalities.

Translational Relevance

OCTA may be useful in detecting early retinal microvascular abnormalities in diabetic patients before the clinical findings of DR become visible.

OCT-Angiography Changes in Patients with Diabetic Macular Edema Treated with Intravitreal Dexamethasone Implant

Purpose

Methods

Results

Conclusions

Perspectives on diabetic retinopathy from advanced retinal vascular imaging

Diabetic retinopathy (DR) is a microvascular complication of diabetes and the most common cause of acquired vision loss in adults worldwide. DR is associated with long-term chronic hyperglycaemia and its detrimental effects on the neurovascular structure and function of the retina. Direct imaging of the retinal vasculature and staging of DR has been traditionally based on fundoscopy and fluorescein angiography, which provide only 2D views of the retina, and in the case of fluorescein angiography, requires an invasive dye injection. In contrast, advanced retinal imaging modalities like optical coherence tomography angiography (OCTA) and adaptive optics (AO) are non-invasive and provide depth-resolved, 3D visualization of retinal vessel structure as well as blood flow. Recent studies utilizing these imaging techniques have shown promise in evaluating quantitative vascular parameters that correlate tightly to clinical DR staging, elucidating functional changes in early diabetes, and monitoring DR treatment response. In this article, we discuss and synthesize the results of advanced retinal imaging studies in DR and their implications for our clinical and pathophysiologic understanding of the disease. Based on the recent literature, we also propose a model to describe the differential changes in vascular structure and flow that have been described on advanced retinal imaging as DR progresses. Future studies of these imaging modalities in larger and more diverse populations, as well as corroboration with histological and functional studies, will be important to further our understanding of DR.

Optical coherence tomography

Optical coherence tomography (OCT) is a non-contact method for imaging the topological and internal microstructure of samples in three dimensions. OCT can be configured as a conventional microscope, as an ophthalmic scanner, or using endoscopes and small diameter catheters for accessing internal biological organs. In this Primer, we describe the principles underpinning the different instrument configurations that are tailored to distinct imaging applications and explain the origin of signal, based on light scattering and propagation. Although OCT has been used for imaging inanimate objects, we focus our discussion on biological and medical imaging. We examine the signal processing methods and algorithms that make OCT exquisitely sensitive to reflections as weak as just a few photons and that reveal functional information in addition to structure. Image processing, display and interpretation, which are all critical for effective biomedical imaging, are discussed in the context of specific applications. Finally, we consider image artifacts and limitations that commonly arise and reflect on future advances and opportunities.

Comparison of Different Metrics for the Identification of Vascular Changes in Diabetic Retinopathy Using OCTA

Retinal vessel metrics identifying microvascular changes such as vessel closure (VC) have shown potential clinical value by identifying eyes with diabetic retinopathy (DR) at different severity levels and at increased risk for disease progression to more severe stages. We compare the performance of 11 different metrics, which include 2 metrics supplied by the manufacturer, based on OCTA for identification of VC in different Early Treatment for Diabetic Retinopathy Study (ETDRS) severity groups. OCTA en-face slabs from 84 healthy eyes (70 ± 4.8 years) and 78 eyes of diabetic individuals (67 ± 7.5 years) were processed using different methods that include abnormal intercapillary spaces (AIS), vessel density (VD), and nine metrics extracted from the en-face slab. The best separation between the eyes with DR and the control group was obtained in the superficial capillary plexus (SCP), with the full retina (FR) also performing well. In the SCP, the metrics that show better performance were the AIS and the VD with a value of area under curve (AUC) equal to 0.89 [95% CI 0.84-0.94] and 0.85 [95% CI 0.79-0.91], respectively, indicating that the VD metric supported by the manufacturer is satisfactory. The values of these metrics on the different ETDRS groups show a progressive increase in VC, which is correlated with disease severity.

Optical coherence tomography (OCT) angiolytics: a review of OCT angiography quantitative biomarkers

Optical coherence tomography angiography (OCTA) provides a non-invasive method to obtain angiography of the chorioretinal vasculature leading to its recent widespread adoption. With a growing number of studies exploring the use of OCTA, various biomarkers quantifying the vascular characteristics have come to light. In the current report, we summarize the biomarkers currently described for retinal and choroidal vasculature using OCTA systems and the methods used to obtain them. Further, we present a critical review of these methods and key findings in common retinal diseases and appraise future directions, including applications of artificial intelligence in OCTA .

Artificial intelligence in OCT angiography

Optical coherence tomographic angiography (OCTA) is a non-invasive imaging modality that provides three-dimensional, information-rich vascular images. With numerous studies demonstrating unique capabilities in biomarker quantification, diagnosis, and monitoring, OCTA technology has seen rapid adoption in research and clinical settings. The value of OCTA imaging is significantly enhanced by image analysis tools that provide rapid and accurate quantification of vascular features and pathology. Today, the most powerful image analysis methods are based on artificial intelligence (AI). While AI encompasses a large variety of techniques, machine-learning-based, and especially deep-learning-based, image analysis provides accurate measurements in a variety of contexts, including different diseases and regions of the eye. Here, we discuss the principles of both OCTA and AI that make their combination capable of answering new questions. We also review contemporary applications of AI in OCTA, which include accurate detection of pathologies such as choroidal neovascularization, precise quantification of retinal perfusion, and reliable disease diagnosis.

Volume rendered 3D OCTA assessment of macular ischemia in patients with type 1 diabetes and without diabetic retinopathy

The aim of this study was to measure macular perfusion in patients with type 1 diabetes and no signs of diabetic retinopathy (DR) using volume rendered three-dimensional (3D) optical coherence tomography angiography (OCTA). We collected data from 35 patients with diabetes and no DR who had OCTA obtained. An additional control group of 35 eyes from 35 healthy subjects was included for comparison. OCTA volume data were processed with a previously presented algorithm in order to obtain the 3D vascular volume and 3D perfusion density. In order to weigh the contribution of different plexuses' impairment to volume rendered vascular perfusion, OCTA en face images were binarized in order to obtain two-dimensional (2D) perfusion density metrics. Mean ± SD age was 27.2 ± 10.2 years [range 19-64 years] in the diabetic group and 31.0 ± 11.4 years [range 19-61 years] in the control group (p = 0.145). The 3D vascular volume was 0.27 ± 0.05 mm³ in the diabetic group and 0.29 ± 0.04 mm³ in the control group (p = 0.020). The 3D perfusion density was 9.3 ± 1.6% and 10.3 ± 1.6% in diabetic patients and controls, respectively (p = 0.005). Using a 2D visualization, the perfusion density was lower in diabetic patients, but only at the deep vascular complex (DVC) level (38.9 ± 3.7% in diabetes and 41.0 ± 3.1% in controls, p = 0.001), while no differences were detected at the superficial capillary plexus (SCP) level (34.4 ± 3.1% and 34.3 ± 3.8% in the diabetic and healthy subjects, respectively, p = 0.899). In conclusion, eyes without signs of DR of patients with diabetes have a reduced volume rendered macular perfusion compared to control healthy eyes.

Quantitative analysis of retinal intermediate and deep capillary plexus in patients with retinal deep vascular complex ischemia

AIM

To quantitatively analyze the retinal intermediate and deep capillary plexus (ICP and DCP) in patients with retinal deep vascular complex ischemia (RDVCI), using 3D projection artifacts removal (3D PAR) optical coherence tomography angiography (OCTA).

METHODS

RDVCI patients and gender- and age-matched healthy controls were assessed and underwent OCTA examinations. The parafoveal vessel density (PFVD) of retinal deep vascular complex (DVC), ICP, and DCP were analyzed, and the percentage of reduction (PR) of PFVD was calculated.

RESULTS

Twenty-four eyes in 22 RDVCI patients (20 in acute phase and 4 in chronic phase) and 24 eyes of 22 healthy subjects were enrolled as the control group. Significant reduction of PFVD in DVC, ICP, and DCP was observed in comparison with the controls (DVC: acute: 43.59%±6.58% vs 49.92%±5.49%, PR=12.69%; chronic: 43.50%±3.33% vs 51.20%±3.80%, PR=15.04%. ICP: acute: 40.28%±7.91% vs 46.97%±7.14%, PR=14.23%; chronic: 41.48%±2.87% vs 46.43%±3.29%, PR=10.66%. DCP: acute: 45.44%±8.27% vs 51.51%±9.97%, PR=11.79%; chronic: 37.78%±3.48% vs 51.73%±5.17%, PR=26.97%; all P<0.05). No significant PR difference was found among DVC, ICP, and DCP of RDVCI in acute phase (P=0.812), but significant difference in chronic phase (P=0.006, DVC vs DCP, ICP vs DCP). No significant difference in PR between acute and chronic phases in the DVC (P=0.735) or ICP (P=0.681) was found, but significant difference in the DCP (P=0.041).

CONCLUSION

The PFVD of DVC, ICP, and DCP in RDVCI is significantly decreased in both acute and chronic phases. ICP impairment is stabilized from acute to chronic phase in RDVCI, whereas subsequent DCP impairment is uncovered and can be explained by ischemia-reperfusion damage.

Detection of the Microvascular Changes of Diabetic Retinopathy Progression Using Optical Coherence Tomography Angiography

Purpose

To investigate microvascular parameters that are related to the severity of diabetic retinopathy (DR) with optical coherence tomography angiography (OCTA).

Methods

In total, 105 eyes from 105 diabetic patients were recruited in this prospective cross-sectional study, including 37 eyes with no clinical signs of DR (NoDR), 43 eyes with nonproliferative diabetic retinopathy (NPDR), and 25 eyes with proliferative diabetic retinopathy (PDR). Angiogram images from the parafoveal superficial capillary plexus (SCP), the deep capillary plexus (DCP), and the radial peripapillary capillary plexus were analyzed, and metrics were compared among groups. Multivariate regression analysis was used to identify the best OCTA parameters that could distinguish DR severity among groups.

Results

Parafoveal vessel diameter index in the SCP and vessel density (VD) in the DCP showed the strongest correlation with the severity of DR (P < 0.01). Extrafoveal avascular area in the SCP was the parameter that could most distinguish NoDR from NPDR (P < 0.01) with sensitivity and specificity of 83.72% and 78.38%, respectively. VD in the DCP also was the most sensitive biomarker to distinguish NPDR from PDR (P < 0.01) with sensitivity and specificity of 84.00% and 79.07%, respectively.

Conclusions

The microvascular changes in the SCP and DCP in DR may have different characteristics that could be identified with specific OCTA parameters. OCTA serves as a promising technology to discriminate eyes with different severity of DR.

Translational Relevance

Our study investigated OCTA metrics and severity of DR. At different stages of DR, ophthalmologists may focus on specific OCTA parameters to predict the progression of retinopathy in individual patients.

Assessing the Use of Incorrectly Scaled Optical Coherence Tomography Angiography Images in Peer-Reviewed Studies: A Systematic Review

Importance

Individual differences in axial length affect the lateral magnification of in vivo retinal images and as a result can affect the accuracy of quantitative measurements made from these images. As measurements from optical coherence tomography angiography (OCTA) images are becoming increasingly used in the diagnosis and monitoring of a wide range of diseases, evaluating which studies use correctly scaled images is crucial to their interpretation.

Objective

To perform a systematic literature review to assess the percentage of articles that report correcting the scale of their OCTA images for individual differences in retinal magnification.

Evidence Review

A PubMed (MEDLINE) search was conducted for articles on OCTA retinal imaging published between June 1, 2015, and June 1, 2018. Initial results included 7552 articles. Initial exclusion criteria removed studies of animal models, as well as reviews, letters, replies, comments, and image-based or photographic essays. Articles not written in English and those that required purchase from non-English language websites were excluded. Articles that did not use OCTA for imaging the retina were also excluded. Remaining articles were reviewed in detail to assess whether the OCTA measurements required correct lateral scaling, and if so, whether axial length was reported or used to scale the images. We also determined the number of articles that mentioned the lack of correct lateral scaling as a limitation of the study.

Findings

A total of 989 articles were included in the detailed review. Of these, 509 were determined to require correct image scaling for their analyses, but only 41 (8.0%) report measuring and using axial length to correct the lateral scale of their OCTA images. Furthermore, of the 468 articles that did not correctly scale their images, only 18 (3.8%) mentioned this as a limitation to their study.

Conclusions and Relevance

These findings suggest that most peer-reviewed articles in PubMed that use quantitative OCTA measurements use incorrectly scaled images. This could call into question the conclusions of such studies and warrants consideration by OCTA manufacturers, physicians, authors, journal reviewers, and journal editors.

Spatial distribution of diabetic capillary non-perfusion

OBJECTIVE

To evaluate the distribution of capillary non-perfusion (CNP) in superficial and deep capillary plexuses (SCP and DCP) in eyes with diabetic retinopathy (DR).

METHODS

In this retrospective case series, macular optical coherence tomography angiography (OCTA) images were obtained from eyes with DR without diabetic macular edema (DME). The area of CNP in SCP and DCP was delineated using an automated approach after excluding the foveal avascular zone (FAZ) and major retinal vessels. The distribution and spatial correlation of the CNP in each layer were analyzed.

RESULTS

Forty-three eyes of 27 patients with DR with a mean age of 59.10 ± 9.05 years were included. The mean CNP area in SCP was statistically significantly higher than DCP (0.722 ± 0.437 mm² vs. 0.184 ± 0.145 mm² , respectively, p < .001). There was a statistically significant association between mean BCVA (0.28 ± 0.21 logMAR) and CNP area in DCP (p = .01). After automated subtraction of CNP areas in DCP from SCP, 25.43 ± 15.05% of CNP areas in the DCP had co-localized CNP areas in SCP. The CNP percentage was statistically significantly different between the concentric rings on foveal center, both in SCP and in DCP (both p < .001) showing a decreasing trend from the outer ring toward the center.

CONCLUSION

In DR, SCP is more ischemic than DCP. This is in contrast to the previously described oxygenation-dependent ischemic cascade following acute retinal vascular occlusions. This study provides further insight into the retinal ischemia in DR.

DcardNet: Diabetic Retinopathy Classification at Multiple Levels Based on Structural and Angiographic Optical Coherence Tomography

OBJECTIVE

Optical coherence tomography (OCT) and its angiography (OCTA) have several advantages for the early detection and diagnosis of diabetic retinopathy (DR). However, automated, complete DR classification frameworks based on both OCT and OCTA data have not been proposed. In this study, a convolutional neural network (CNN) based method is proposed to fulfill a DR classification framework using en face OCT and OCTA.

METHODS

A densely and continuously connected neural network with adaptive rate dropout (DcardNet) is designed for the DR classification. In addition, adaptive label smoothing was proposed and used to suppress overfitting. Three separate classification levels are generated for each case based on the International Clinical Diabetic Retinopathy scale. At the highest level the network classifies scans as referable or non-referable for DR. The second level classifies the eye as non-DR, non-proliferative DR (NPDR), or proliferative DR (PDR). The last level classifies the case as no DR, mild and moderate NPDR, severe NPDR, and PDR.

RESULTS

We used 10-fold cross-validation with 10% of the data to assess the network's performance. The overall classification accuracies of the three levels were 95.7%, 85.0%, and 71.0% respectively.

CONCLUSION/SIGNIFICANCE

A reliable, sensitive and specific automated classification framework for referral to an ophthalmologist can be a key technology for reducing vision loss related to DR.

Macular Ischemia Quantification Using Deep-Learning Denoised Optical Coherence Tomography Angiography in Branch Retinal Vein Occlusion

Purpose

To examine whether deep-learning denoised optical coherence tomography angiography (OCTA) images could enhance automated macular ischemia quantification in branch retinal vein occlusion (BRVO).

Methods

This retrospective, single-center, cross-sectional study enrolled 74 patients with BRVO and 46 age-matched healthy subjects. The severity of macular ischemia was graded as mild, moderate, or severe. Denoised OCTA images were produced using a neural network model. Quantitative parameters derived from denoised images, including vessel density and nonperfusion area, were compared with those derived from the OCTA machine. The main outcome measures were correlations between quantitative parameters, and areas under receiver operating characteristic curves (AUCs) in classifying the severity of the macular ischemia.

Results

The vessel density and nonperfusion area from denoised images were correlated strongly with the corresponding parameters from machine-derived images in control eyes and BRVO eyes with mild or moderate macular ischemia (all P < 0.001). However, no such correlation was found in eyes with severe macular ischemia. The vessel density and nonperfusion area from denoised images had significantly larger area under receiver operating characteristic curve than those derived from the original images in classifying moderate versus severe macular ischemia (0.927 vs 0.802 [P = 0.042] and 0.946 vs 0.797, [P = 0.022], respectively). There were no significant differences in the areas under receiver operating characteristic curve between the denoised images and the machine-derived parameters in classifying control versus BRVO, and mild versus moderate macular ischemia.

Conclusions

A neural network model is useful for removing speckle noise on OCTA images and facilitating the automated grading of macular ischemia in eyes with BRVO.

Translational Relevance

Deep-learning denoised optical coherence tomography angiography images could enhance automated macular ischemia quantification.

Quantification of Nonperfusion Area in Montaged Widefield OCT Angiography Using Deep Learning in Diabetic Retinopathy

Purpose

To examine the efficacy of a deep learning-based algorithm to quantify the nonperfusion area (NPA) on montaged widefield OCT angiography (OCTA) for assessment of diabetic retinopathy (DR) severity.

Design

Cross-sectional study.

Participants

One hundred thirty-seven participants with a full range of DR severity and 26 healthy participants.

Methods

A deep learning-based algorithm was developed for detecting and quantifying NPA in the superficial vascular complex on widefield OCTA comprising 3 horizontally montaged 6 × 6-mm OCTA scans from the nasal, macular, and temporal regions. We trained the algorithm on 978 volumetric OCTA scans from all participants using 5-fold cross-validation. The algorithm can distinguish NPA from shadow artifacts. The F1 score evaluated segmentation accuracy. The area under the receiver operating characteristic curve and sensitivity with specificity fixed at 95% quantified network performance to distinguish patients with diabetes from healthy control participants, referable DR from nonreferable DR (nonproliferative DR [NPDR] less than moderate severity), and severe DR (severe NPDR, proliferative DR, or DR with edema) from nonsevere DR (mild to moderate NPDR).

Main Outcome Measures

Widefield OCTA NPA, visual acuity (VA), and DR severities.

Results

Automatically segmented NPA showed high agreement with the manually delineated ground truth, with a mean ± standard deviation F1 score of 0.78 ± 0.05 in nasal, 0.82 ± 0.07 in macular, and 0.78 ± 0.05 in temporal scans. The extrafoveal avascular area (EAA) in the macular scan showed the best sensitivity at 54% for differentiating those with diabetes from healthy control participants, whereas montaged widefield OCTA scan showed significantly higher sensitivity than macular scans (P < 0.0001, McNemar’s test) for detecting eyes with DR at 66%, referable DR at 63%, and severe DR at 62%. Montaged widefield OCTA showed the highest correlation (Spearman ρ = 0.74; P < 0.0001) between EAA and DR severity. The macular scan showed the strongest negative correlation (Pearson ρ = –0.42; P < 0.0001) between EAA and best-corrected VA.

Conclusions

A deep learning-based algorithm for montaged widefield OCTA can detect NPA accurately and can improve the detection of clinically important DR.

DETECTION OF CLINICALLY UNSUSPECTED RETINAL NEOVASCULARIZATION WITH WIDE-FIELD OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY

PURPOSE

To evaluate wide-field optical coherence tomography angiography (OCTA) for detection of clinically unsuspected neovascularization (NV) in diabetic retinopathy (DR).

METHODS

This prospective observational single-center study included adult patients with a clinical diagnosis of nonproliferative DR. Participants underwent a clinical examination, standard 7-field color photography, and OCTA with commercial and prototype swept-source devices. The wide-field OCTA was achieved by montaging five 6 × 10-mm scans from a prototype device into a 25 × 10-mm image and three 6 × 6-mm scans from a commercial device into a 15 × 6-mm image. A masked grader determined the retinopathy severity from color photographs. Two trained readers examined conventional and wide-field OCTA images for the presence of NV.

RESULTS

Of 27 participants, photographic grading found 13 mild, 7 moderate, and 7 severe nonproliferative DR. Conventional 6 × 6-mm OCTA detected NV in 2 eyes (7%) and none with 3 × 3-mm scans. Both prototype and commercial wide-field OCTA detected NV in two additional eyes. The mean area of NV was 0.38 mm (range 0.17-0.54 mm). All eyes with OCTA-detected NV were photographically graded as severe nonproliferative DR.

CONCLUSION

Wide-field OCTA can detect small NV not seen on clinical examination or color photographs and may improve the clinical evaluation of DR.

Optical Coherence Tomography Angiography of the Choriocapillaris in Age-Related Macular Degeneration

The advent of optical coherence tomography angiography (OCTA) has allowed for remarkable advancements in our understanding of the role of the choriocapillaris in age-related macular degeneration (AMD). As a relatively new imaging modality, techniques to analyze and quantify choriocapillaris images are still evolving. Quantification of the choriocapillaris requires careful consideration of many factors, including the type of OCTA device, segmentation of the choriocapillaris slab, image processing techniques, and thresholding method. OCTA imaging shows that the choriocapillaris is impaired in intermediate non-neovascular AMD, and the severity of impairment may predict the advancement of disease. In advanced atrophic AMD, the choriocapillaris is severely impaired underneath the area of geographic atrophy, and the level of impairment surrounding the lesion predicts the rate of atrophy enlargement. Macular neovascularization can be readily identified and classified using OCTA, but it is still unclear if neovascularization features with OCTA can predict the lesion’s level of activity. The choriocapillaris surrounding macular neovascularization is impaired while the more peripheral choriocapillaris is spared, implying that choriocapillaris disruption may drive neovascularization growth. With continued innovation in OCTA image acquisition and analysis methods, advancement in clinical applications and pathophysiologic discoveries in AMD are set to follow.

Vascular Density of Deep, Intermediate and Superficial Vascular Plexuses Are Differentially Affected by Diabetic Retinopathy Severity

Purpose

The purpose of this study was to evaluate differences in optical coherence tomography angiography (OCTA) metrics in the superficial (SCP), intermediate (ICP), and deep (DCP) vascular plexuses across diabetic retinopathy (DR) severity levels.

Methods

This was a cross sectional observational retrospective chart review study. Eligible patients with diabetes who underwent same day RTVue XR Avanti OCTA, spectral-domain optical coherence tomography (SD-OCT), and 200-degree Optos ultrawide field color imaging. SCP, ICP, and DCP vessel density (VD) and vessel length density (VLD) were assessed using 3-D projection artifact removal software (PAROCTA) software.

Results

Of 396 eyes (237 patients), 16.1% had no DR, 26.9% mild nonproliferative DR (NPDR), 21.1% moderate NPDR, 12.1% severe NPDR, 10.1% proliferative DR (PDR) without panretinal photocoagulation (PRP), and 13.4% PDR with PRP. When comparing mild NPDR to no DR eyes, ICP and DCP VD and VLD were significantly lower, but there was no difference for SCP metrics. In eyes with more severe DR, there were significant differences in SCP VD and VLD between DR severity levels (mild versus moderate NPDR: VD 35.45 ± 3.31 vs. 34.14 ± 3.38, P = 0.008 and VLD 17.59 ± 1.83 vs. 16.80 ± 1.83, P = 0.003; moderate versus severe NPDR: VLD 16.80 ± 1.83 vs. 15.79 ± 1.84, P = 0.019), but no significant differences in ICP or DCP.

Conclusions

Although VD of each of the three individual layers decreases with increasing DR severity, DR severity has a substantially different effect on OCTA parameters within each layer. Vascular changes in eyes with no to early DR were present primarily in the deeper vascular layers, whereas in eyes with advanced DR the opposite was observed. This study highlights the effects of ICP and the importance of assessing SCP and DCP changes independently across each DR severity level.

Plexus-specific retinal vascular anatomy and pathologies as seen by projection-resolved optical coherence tomographic angiography

Optical coherence tomographic angiography (OCTA) is a novel technology capable of imaging retinal vasculature three-dimensionally at capillary scale without the need to inject any extrinsic dye contrast. However, projection artifacts cause superficial retinal vascular patterns to be duplicated in deeper layers, thus interfering with the clean visualization of some retinal plexuses and vascular pathologies. Projection-resolved OCTA (PR-OCTA) uses post-processing algorithms to reduce projection artifacts. With PR-OCTA, it is now possible to resolve up to 4 distinct retinal vascular plexuses in the living human eye. The technology also allows us to detect and distinguish between various retinal and optic nerve diseases. For example, optic nerve diseases such as glaucoma primarily reduces the capillary density in the superficial vascular complex, which comprises the nerve fiber layer plexus and the ganglion cell layer plexus. Outer retinal diseases such as retinitis pigmentosa primarily reduce the capillary density in the deep vascular complex, which comprises the intermediate capillary plexus and the deep capillary plexus. Retinal vascular diseases such as diabetic retinopathy and vein occlusion affect all plexuses, but with different patterns of capillary loss and vascular malformations. PR-OCTA is also useful in distinguishing various types of choroidal neovascularization and monitoring their response to anti-angiogenic medications. In retinal angiomatous proliferation and macular telangiectasia type 2, PR-OCTA can trace the pathologic vascular extension into deeper layers as the disease progress through stages. Plexus-specific visualization and measurement of retinal vascular changes are improving our ability to diagnose, stage, monitor, and assess treatment response in a wide variety of optic nerve and retinal diseases. These applications will be further enhanced with the continuing improvement of the speed and resolution of the OCT platforms, as well as the development of software algorithms to reduce artifacts, improve image quality, and make quantitative measurements.

Automated segmentation of retinal nonperfusion area in fluorescein angiography in retinal vein occlusion using convolutional neural networks

PURPOSE

Retinal vein occlusion (RVO) is the second most common cause of vision loss after diabetic retinopathy due to retinal vascular disease. Retinal nonperfusion (RNP), identified on fluorescein angiograms (FA) and appearing as hypofluorescence regions, is one of the most significant characteristics of RVO. Quantification of RNP is crucial for assessing the severity and progression of RVO. However, in current clinical practice, it is mostly conducted manually, which is time-consuming, subjective, and error-prone. The purpose of this study is to develop fully automated methods for segmentation of RNP using convolutional neural networks (CNNs).

METHODS

FA images from 161 patients were analyzed, and RNP areas were annotated by three independent physicians. The optimal method to use multi-physicians' labeled data to train the CNNs was evaluated. An adaptive histogram-based data augmentation method was utilized to boost the CNN performance. CNN methods based on context encoder module were developed for automated segmentation of RNP and compared with existing state-of-the-art methods.

RESULTS

The proposed methods achieved excellent agreements with physicians for segmentation of RNP in FA images. The CNN performance can be improved significantly by the proposed adaptive histogram-based data augmentation method. Using the averaged labels from physicians to train the CNNs achieved the best consensus with all physicians, with a mean accuracy of 0.883±0.166 with fivefold cross-validation.

CONCLUSIONS

We reported CNN methods to segment RNP in RVO in FA images. Our work can help improve clinical workflow, and can be useful for further investigating the association between RNP and retinal disease progression, as well as for evaluating the optimal treatments for the management of RVO.

Automated Segmentation of Optical Coherence Tomography Angiography Images: Benchmark Data and Clinically Relevant Metrics

Purpose

To generate the first open dataset of retinal parafoveal optical coherence tomography angiography (OCTA) images with associated ground truth manual segmentations, and to establish a standard for OCTA image segmentation by surveying a broad range of state-of-the-art vessel enhancement and binarization procedures.

Methods

Handcrafted filters and neural network architectures were used to perform vessel enhancement. Thresholding methods and machine learning approaches were applied to obtain the final binarization. Evaluation was performed by using pixelwise metrics and newly proposed topological metrics. Finally, we compare the error in the computation of clinically relevant vascular network metrics (e.g., foveal avascular zone area and vessel density) across segmentation methods.

Results

Our results show that, for the set of images considered, deep learning architectures (U-Net and CS-Net) achieve the best performance (Dice = 0.89). For applications where manually segmented data are not available to retrain these approaches, our findings suggest that optimally oriented flux (OOF) is the best handcrafted filter (Dice = 0.86). Moreover, our results show up to 25% differences in vessel density accuracy depending on the segmentation method used.

Conclusions

In this study, we derive and validate the first open dataset of retinal parafoveal OCTA images with associated ground truth manual segmentations. Our findings should be taken into account when comparing the results of clinical studies and performing meta-analyses. Finally, we release our data and source code to support standardization efforts in OCTA image segmentation.

Translational Relevance

This work establishes a standard for OCTA retinal image segmentation and introduces the importance of evaluating segmentation performance in terms of clinically relevant metrics.

Plexus-specific retinal capillary avascular area in exudative age-related macular degeneration with projection-resolved OCT angiography

OBJECTIVE

To detect the plexus-specific retinal capillary avascular area in exudative age-related macular degeneration (EAMD) with projection-resolved optical coherence tomography angiography (PR-OCTA).

METHODS AND ANALYSIS

In this prospective cross-sectional single centre study, eyes with treatment-naïve EAMD underwent macular 3×3 mm OCTA with AngioVue system. OCTA scans were analysed and processed including three-dimensional projection artefact removal, retinal layer semi-automated segmentation and en face angiogram generation. Automated quantification of extrafoveal (excluding the central 1 mm circle) avascular area (EAA) were calculated on projection-resolved superficial vascular complex (SVC), intermediate capillary plexus (ICP) and deep capillary plexus (DCP), respectively.

RESULTS

Nineteen eyes with EAMD and 19 age-matched healthy control eyes were included. There was no significant difference between the EAMD and control eyes in terms of age, sex, axial length and mean ocular perfusion pressure (all p>0.05). Compared with control eyes, EAMD eyes had significantly larger EAA in SVC (median 0.125 vs 0.059 mm², p=0.006), ICP (0.016 vs 0.000 mm², p=0.004) and DCP (0.033 vs 0.000 mm², p＜0.001).

CONCLUSION

PR-OCTA showed that EAMD is associated with focal avascular area in all the three retinal vascular plexuses.

Superficial capillary perfusion on optical coherence tomography angiography differentiates moderate and severe nonproliferative diabetic retinopathy

Purpose

To identify objective optical coherence tomography angiography (OCTA) parameters that characterize the spectrum of non-proliferative diabetic retinopathy (NPDR), especially those that distinguish moderate from severe NPDR.

Methods

Sixty eyes of 60 patients with treatment-naïve NPDR (mild: 21, moderate: 21, severe: 18), 23 eyes with diabetes and no retinopathy, and 24 healthy control eyes were enrolled. OCTA slabs were segmented into superficial (SCP), middle (MCP), and deep capillary plexus (DCP) and thresholded by a new method based on DCP skeletonized vessel length. The foveal avascular zone (FAZ) area, parafoveal vessel density (VD), and adjusted flow index (AFI) from all three capillary layers and the vessel length density (VLD) of the SCP were compared between each severity group, after adjusting for age and image quality.

Results

All vessel density markers decreased with increasing severity of NPDR. SCP VD and VLD demonstrated significant differences between eyes with diabetes with no retinopathy and mild NPDR (p = 0.001 and p < 0.001, respectively), as well as between moderate vs. severe NPDR (p = 0.004 and p = 0.009, respectively). MCP VD significantly decreased between moderate and severe NPDR (p = 0.01). AFI significantly increased in the SCP and showed a decreasing trend in the MCP and DCP with increasing NPDR severity.

Conclusions

Changes in the SCP VD, SCP VLD, and MCP VD can distinguish severe NPDR from lower-risk stages. SCP changes may be more reliable due to their lower susceptibility to noise and projection artifacts. Thresholding OCTA images based on DCP skeletonized vessel length showed less variability in moderate and severe NPDR. Additional studies are warranted to validate this new thresholding method.

Approaches to quantify optical coherence tomography angiography metrics

Optical coherence tomography (OCT) has revolutionized the field of ophthalmology in the last three decades. As an OCT extension, OCT angiography (OCTA) utilizes a fast OCT system to detect motion contrast in ocular tissue and provides a three-dimensional representation of the ocular vasculature in a non-invasive, dye-free manner. The first OCT machine equipped with OCTA function was approved by U.S. Food and Drug Administration in 2016 and now it is widely applied in clinics. To date, numerous methods have been developed to aid OCTA interpretation and quantification. In this review, we focused on the workflow of OCTA-based interpretation, beginning from the generation of the OCTA images using signal decorrelation, which we divided into intensity-based, phase-based and phasor-based methods. We further discussed methods used to address image artifacts that are commonly observed in clinical settings, to the algorithms for image enhancement, binarization, and OCTA metrics extraction. We believe a better grasp of these technical aspects of OCTA will enhance the understanding of the technology and its potential application in disease diagnosis and management. Moreover, future studies will also explore the use of ocular OCTA as a window to link ocular vasculature to the function of other organs such as the kidney and brain.

Optical Coherence Tomography Angiography Avascular Area Association With 1-Year Treatment Requirement and Disease Progression in Diabetic Retinopathy

PURPOSE

To assess the association between optical coherence tomography angiography (OCTA)-quantified avascular areas (AAs) and diabetic retinopathy (DR) severity, progression, and treatment requirement in the following year.

DESIGN

Prospective cohort study.

METHODS

We recruited patients with diabetes from a tertiary academic retina practice and obtained 3-mm × 3-mm macular OCTA scans with the AngioVue system and standard 7-field color photographs at baseline and at a 1-year follow-up visit. A masked grader determined the severity of DR from the color photographs using the Early Treatment of Diabetic Retinopathy scale. A custom algorithm detected extrafoveal AA (EAA) excluding the central 1-mm circle in projection-resolved superficial vascular complex (SVC), intermediate capillary plexus (ICP), and deep capillary plexus (DCP).

RESULTS

Of 138 patients, 92 (41 men, ranging in age from 26-84 years [mean 59.4 years]) completed 1 year of follow-up. At baseline, EAAs for SVC, ICP, and DCP were all significantly correlated with retinopathy severity (P < .0001). DCP EAA was significantly associated with worse visual acuity (r = -0.24, P = .02), but SVC and ICP EAA were not. At 1 year, 11 eyes progressed in severity by at least 1 step. Multivariate logistic regression analysis demonstrated the progression was significantly associated with baseline SVC EAA (odds ratio = 8.73, P = .04). During the follow-up period, 33 eyes underwent treatment. Multivariate analysis showed that treatment requirement was significantly associated with baseline DCP EAA (odds ratio = 3.39, P = .002). No baseline metric was associated with vision loss at 1 year.

CONCLUSIONS

EAAs detected by OCTA in diabetic eyes are significantly associated with baseline DR severity, disease progression, and treatment requirement over 1 year.

Macular microvascular changes after intravitreal bevacizumab injection in diabetic macular edema

OBJECTIVE

To evaluate the changes in retinal capillary plexus and the choriocapillaris after a single intravitreal injection of bevacizumab in eyes with diabetic macular edema using optical coherence tomography angiography (OCTA).

DESIGN

Prospective interventional case series.

PARTICIPANTS

Patients having diabetes with centre-involving diabetic macular edema.

METHODS

In this prospective interventional case series, eyes with centre-involving diabetic macular edema were enrolled. Vascular density (VD), vascular diameter index (VDI), vascular length density (VLD), foveal avascular zone (FAZ) area, and foveal density (FD)-300 were measured using en face OCTA images before and 1 month after administration of intravitreal bevacizumab. VD and VDI measurements were performed in the superficial capillary plexus (SCP) and deep retinal capillary plexus (DCP) and in the choriocapillaris. Additionally, capillary nonperfusion area (CNPA) was detected automatically based on vessel distance map in 4 concentric rings around the foveal centre. The segmentation error was manually corrected, and the measurements were performed by 2 expert graders.

RESULTS

Twenty-three eyes of 19 patients with a mean age of 62.76 ± 6.88 years were included. There were no significant changes in the FAZ area, FD-300, or in the VD of the foveal and parafoveal SCP and DCP. Also, VLD and VDI of the SCP and DCP remained unchanged. The change in the CNPA was not statistically significant. The VD of choriocapillaris increased significantly after injections (p = 0.005).

CONCLUSIONS

FAZ area and VD of the retinal capillary plexus remained stable in the short-term period after intravitreal bevacizumab. In addition, the choriocapillaris blood flow improved after bevacizumab injection.