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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.

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.

Purpose: Microaneurysms (MAs) have distinct, oval-shaped, hyperreflective walls on structural OCT, and inconsistent flow signal in the lumen with OCT angiography (OCTA). Their relationship to regional macular edema in diabetic retinopathy (DR) has not been quantitatively explored., Design: Retrospective, cross-sectional study., Participants: A total of 99 participants, including 23 with mild, nonproliferative DR (NPDR), 25 with moderate NPDR, 34 with severe NPDR, and 17 with proliferative DR., Methods: We obtained 3 × 3-mm scans with a commercial device (Solix, Visionix/Optovue) in 99 patients with DR. Trained graders manually identified MAs and their location relative to the anatomic layers from cross-sectional OCT. Microaneurysms were first classified as perfused if flow signal was present in the OCTA channel. Then, perfused MAs were further classified into fully and partially perfused MAs based on the flow characteristics in en face OCTA. The presence of retinal fluid based on OCT near MAs was compared between perfused and nonperfused types. We also compared OCT-based MA detection to fundus photography (FP)- and fluorescein angiography (FA)-based detection., Main Outcome Measures: OCT-identified MAs can be classified according to colocalized OCTA flow signal into fully perfused, partially perfused, and nonperfused types. Fully perfused MAs may be more likely to be associated with diabetic macular edema (DME) than those without flow., Results: We identified 308 MAs (166 fully perfused, 88 partially perfused, 54 nonperfused) in 42 eyes using OCT and OCTA. Nearly half of the MAs identified in this study straddle the inner nuclear layer and outer plexiform layer. Compared with partially perfused and nonperfused MAs, fully perfused MAs were more likely to be associated with local retinal fluid. The associated fluid volumes were larger with fully perfused MAs compared with other types. OCT/OCTA detected all MAs found on FP. Although not all MAs seen with FA were identified with OCT, some MAs seen with OCT were not visible with FA or FP., Conclusions: OCT-identified MAs with colocalized flow on OCTA are more likely to be associated with DME than those without flow., Financial Disclosure(s): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article., (Copyright © 2023 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.)

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.

Purpose: To assess the value of en face OCT for detecting clinically unsuspected retinal neovascularization (RNV) in patients with nonproliferative diabetic retinopathy (NPDR)., Design: A retrospective, cross-sectional study., Participants: Treatment-naïve patients clinically graded as NPDR in an ongoing prospective observational OCT angiography (OCTA) study at a tertiary care center., Methods: Each patient underwent imaging of 1 eye with a spectral-domain OCTA, generating a 17 × 17-mm widefield image by montaging four 9 × 9-mm scans. Two independent graders examined a combination of en face OCT, en face OCTA with a custom vitreoretinal interface slab, and cross-sectional OCTA to determine the presence of RNV. We measured the area of RNV flow within RNV lesions on en face OCTA., Main Outcome Measures: Detection rate of clinically occult RNV with OCT and OCTA., Results: Of 63 enrolled eyes, 27 (43%) were clinically graded as severe NPDR, 16 (25%) as moderate NPDR, and 20 (32%) as mild NPDR. Using the combination of en face OCT, en face OCTA, and cross-sectional OCTA, the graders detected 42 RNV lesions in 12 (19%) eyes, of which 8 (67%) were graded as severe NPDR, 2 (17%) as moderate NPDR, and 2 (17%) as mild NPDR. The sensitivity of en face OCT alone for detecting eyes with RNV was similar to that of en face OCTA alone (100% vs. 92%; P = 0.32), whereas the specificity of en face OCT alone was significantly lower than that of en face OCTA alone (32% vs. 73%; P < 0.001). For detecting individual RNV lesions, the en face OCT was 100% sensitive, compared with 67% sensitivity for the en face OCTA (P < 0.001). The area of RNV lesions that manual grading with en face OCTA alone missed was significantly smaller than that of manually detectable RNV (Mean [standard deviation] RNV flow area, 0.015 [0.020] mm 2 vs. 0.16 [0.36] mm 2 ; P < 0.001)., Conclusion: The combination of en face OCT and OCTA can detect clinically occult RNV with high sensitivity. For screening these small lesions, en face OCT may be a useful imaging modality., Financial Disclosure(s): Proprietary or commercial disclosure may be found after the references., (Copyright © 2023 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.)

Synopsis: A deep-learning-based macular extrafoveal avascular area (EAA) on a 6×6 mm optical coherence tomography (OCT) angiogram is less dependent on the signal strength and shadow artefacts, providing better diagnostic accuracy for diabetic retinopathy (DR) severity than the commercial software measured extrafoveal vessel density (EVD)., Aims: To compare a deep-learning-based EAA to commercial output EVD in the diagnostic accuracy of determining DR severity levels from 6×6 mm OCT angiography (OCTA) scans., Methods: The 6×6 mm macular OCTA scans were acquired on one eye of each participant with a spectral-domain OCTA system. After excluding the central 1 mm diameter circle, the EAA on superficial vascular complex was measured with a deep-learning-based algorithm, and the EVD was obtained with commercial software., Results: The study included 34 healthy controls and 118 diabetic patients. EAA and EVD were highly correlated with DR severity (ρ=0.812 and -0.577, respectively, both p<0.001) and visual acuity (r=-0.357 and 0.420, respectively, both p<0.001). EAA had a significantly (p<0.001) higher correlation with DR severity than EVD. With the specificity at 95%, the sensitivities of EAA for differentiating diabetes mellitus (DM), DR and severe DR from control were 80.5%, 92.0% and 100.0%, respectively, significantly higher than those of EVD 11.9% (p=0.001), 13.6% (p<0.001) and 15.8% (p<0.001), respectively. EVD was significantly correlated with signal strength index (SSI) (r=0.607, p<0.001) and shadow area (r=-0.530, p<0.001), but EAA was not (r=-0.044, p=0.805 and r=-0.046, p=0.796, respectively). Adjustment of EVD with SSI and shadow area lowered sensitivities for detection of DM, DR and severe DR., Conclusion: Macular EAA on 6×6 mm OCTA measured with a deep learning-based algorithm is less dependent on the signal strength and shadow artefacts, and provides better diagnostic accuracy for DR severity than EVD measured with the instrument-embedded software., Competing Interests: Competing interests: YJ and STB (financial support) have a significant financial interest in Optovue Inc. These potential conflicts of interest have been reviewed and are managed by OHSU. The other authors do not have any potential financial conflicts of interest., (© Author(s) (or their employer(s)) 2023. No commercial re-use. See rights and permissions. Published by BMJ.)

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.

Purpose: In diabetic macular edema (DME), the correlation between visual acuity (VA) and central subfield thickness (CST) is weak. We hypothesize that fluid volume (FV) in the inner nuclear layer (INL) may correlate more strongly with VA., Design: Retrospective, cross-sectional study., Methods: One eye each of diabetic patients with DME was included. We measured intraretinal fluid volume that was detected by automated fluid detection algorithm on 3- × 3-mm optical coherence tomography angiogram volume scans. The detected fluid was subdivided into inner FV, bounded by the INL, and outer FV, the fluid between the outer border of INL to the ellipsoid zone., Results: We enrolled 125 patients with DME (60 women; mean age, 61 years). The mean detected inner FV was 0.013 mm 3 in 109 eyes (87%). The mean detected outer FV was 0.042 mm 3 in 124 eyes (99%). Univariate analysis demonstrated that the VA significantly correlated with the inner FV (P < .0001), whole macular FV (P = .010), and CST (P = .036). Multivariate analysis demonstrated that the inner FV was the only significant factor (β = -0.41, P = .004). These correlations were consistent when the treatment-naïve group (n = 33) and the eyes without previous laser treatments (n = 93) were analyzed separately. The area under the receiver operating characteristic curve of inner FV for VA of 20/32 or worse was significantly higher than that for CST (0.66 vs 0.54, P = .018)., Conclusions: The inner FV has a stronger association with VA than other OCT biomarkers in DME and may be more clinically useful., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Purpose: We propose a deep learning-based image reconstruction algorithm to produce high-resolution optical coherence tomographic angiograms (OCTA) of the intermediate capillary plexus (ICP) and deep capillary plexus (DCP)., Methods: In this study, 6-mm × 6-mm macular scans with a 400 × 400 A-line sampling density and 3-mm × 3-mm scans with a 304 × 304 A-line sampling density were acquired on one or both eyes of 180 participants (including 230 eyes with diabetic retinopathy and 44 healthy controls) using a 70-kHz commercial OCT system (RTVue-XR; Optovue, Inc., Fremont, California, USA). Projection-resolved OCTA algorithm removed projection artifacts in voxel. ICP and DCP angiograms were generated by maximum projection of the OCTA signal within the respective plexus. We proposed a deep learning-based method, which receives inputs from registered 3-mm × 3-mm ICP and DCP angiograms with proper sampling density as the ground truth reference to reconstruct 6-mm × 6-mm high-resolution ICP and DCP en face OCTA. We applied the same network on 3-mm × 3-mm angiograms to enhance these images further. We evaluated the reconstructed 3-mm × 3-mm and 6-mm × 6-mm angiograms based on vascular connectivity, Weber contrast, false flow signal (flow signal erroneously generated from background), and the noise intensity in the foveal avascular zone., Results: Compared to the originals, the Deep Capillary Angiogram Reconstruction Network (DCARnet)-enhanced 6-mm × 6-mm angiograms had significantly reduced noise intensity (ICP, 7.38 ± 25.22, P < 0.001; DCP, 11.20 ± 22.52, P < 0.001), improved vascular connectivity (ICP, 0.95 ± 0.01, P < 0.001; DCP, 0.96 ± 0.01, P < 0.001), and enhanced Weber contrast (ICP, 4.25 ± 0.10, P < 0.001; DCP, 3.84 ± 0.84, P < 0.001), without generating false flow signal when noise intensity lower than 650. The DCARnet-enhanced 3-mm × 3-mm angiograms also reduced noise, improved connectivity, and enhanced Weber contrast in 3-mm × 3-mm ICP and DCP angiograms from 101 eyes. In addition, DCARnet preserved the appearance of the dilated vessels in the reconstructed angiograms in diabetic eyes., Conclusions: DCARnet can enhance 3-mm × 3-mm and 6-mm × 6-mm ICP and DCP angiogram image quality without introducing artifacts., Translational Relevance: The enhanced 6-mm × 6-mm angiograms may be easier for clinicians to interpret qualitatively.

This study was designed to evaluate iVue Spectral-domain optical coherence tomography (SD-OCT) effectiveness in screening for eye disease compared to clinical examination. Subjects were recruited from the Casey Eye Community Outreach Program Mobile Clinic during its routinely scheduled outreach clinics to indigent, underserved populations throughout Oregon. Macular optical coherence tomography interpretation and automated optical coherence tomography analysis were compared to the clinical examination, with specific attention to findings indicative of retinal abnormalities, risks for glaucoma, and narrow angles. As a result, a total of 114 subjects were included in this study. In diabetics, optical coherence tomography and clinical exam were in fair agreement (kappa = 0.39), with 22% of eyes having abnormal findings on macular optical coherence tomography and 26% of eyes having diabetic retinopathy or diabetic macular edema on fundus exam. In non-diabetics, optical coherence tomography and clinical exam were in fair agreement (kappa = 0.28), with 11% of eyes having abnormal findings on macular optical coherence tomography and 9% on fundus exam. Using optical coherence tomography ganglion cell complex and retinal nerve fiber layer analysis, 18% of eyes were found to be glaucoma suspects, whereas clinical exam of cup-to-disc ratio detected 8% and intraocular pressure 5%. Agreements between optical coherence tomography and other methods were poor (kappa < 0.11) for glaucoma suspect. Anterior segment optical coherence tomography of the angle found 8% of eyes to have occludable angles, whereas slit lamp and gonioscopy found 5% of eyes to have narrow angles, with moderate agreement (kappa = 0.57). In summary, optical coherence tomography detected additional retinal abnormalities, glaucoma suspects, and narrow angles compared to clinical exam alone and may serve as a useful adjunct to the clinical exam in screening for eye disease in a low-risk, medically underserved, ethnically diverse population.

Importance: Diabetic macular edema (DME) is the predominant cause of visual impairment in patients with type 1 or 2 diabetes. Automated fluid volume measurements using optical coherence tomography (OCT) may improve the diagnostic accuracy of DME screening., Objective: To assess the diagnostic accuracy of an automated central macular fluid volume (CMFV) quantification using OCT for DME., Design, Setting, and Participants: A cross-sectional observational study was conducted at a tertiary academic center among 215 patients with diabetes (1 eye each) enrolled from January 26, 2015, to December 23, 2019. All participants underwent comprehensive examinations, 6 × 6-mm macular structural OCT horizontal raster scans, and 6 × 6-mm macular OCT angiography volumetric scans. From January 1 to March 30, 2020, 2 retinal specialists reviewed the structural OCT scans independently and diagnosed DME if intraretinal or subretinal fluid was present. Diabetic macular edema was considered center involved if fluid was present within the central fovea (central 1-mm circle). A third retinal specialist arbitrated any discrepancy. The mean central subfield thickness (CST) within the central fovea was measured on structural OCT horizontal raster scans. A deep learning algorithm automatically quantified fluid volumes on 6 × 6-mm OCT angiography volumetric scans and within the central foveas (CMFV)., Main Outcomes and Measures: The area under the receiver operating characteristic curve (AUROC) and the sensitivity and specificity of CST and CMFV for DME diagnosis., Results: We enrolled 1 eye each of 215 patients with diabetes (117 women [54.4%]; mean [SD] age, 59.6 [12.4] years). Diabetic macular edema was present in 136 eyes; 93 cases of DME were center involved. The AUROC of CMFV for diagnosis of center-involved DME (0.907 [95% CI, 0.861-0.954]) was greater than the AUROC of CST (0.832 [95% CI, 0.775-0.889]; P = .02). With the specificity set at 95%, the sensitivity of CMFV for detection of center-involved DME (78.5% [95% CI, 68.8%-86.3%]) was higher than that of CST (53.8% [95% CI, 43.1%-64.2%]; P = .002). Center-involved DME cases not detected by CST but detected by CMFV were associated with a thinner CST (290.8 μm [95% CI, 282.3-299.3 μm] vs 369.4 μm [95% CI, 347.1-391.7 μm]; P < .001), higher proportion of previous macular laser treatment (11 of 28 [39.3%; 95% CI, 21.5%-59.4%] vs 12 of 65 [18.5%; 95% CI, 9.9%-30.0%]; P = .03), and female sex (20 of 28 [71.4%; 95% CI, 51.3%-86.8%] vs 31 of 65 [47.7%; 95% CI, 35.1%-60.5%]; P = .04)., Conclusions and Relevance: These findings suggest that an automated CMFV is a more accurate diagnostic biomarker than CST for DME and may improve screening for DME.

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.

Purpose: We proposed a deep convolutional neural network (CNN), named Retinal Fluid Segmentation Network (ReF-Net), to segment retinal fluid in diabetic macular edema (DME) in optical coherence tomography (OCT) volumes., Methods: The 3- × 3-mm OCT scans were acquired on one eye by a 70-kHz OCT commercial AngioVue system (RTVue-XR; Optovue, Inc., Fremont, CA, USA) from 51 participants in a clinical diabetic retinopathy (DR) study (45 with retinal edema and six healthy controls, age 61.3 ± 10.1 (mean ± SD), 33% female, and all DR cases were diagnosed as severe NPDR or PDR). A CNN with U-Net-like architecture was constructed to detect and segment the retinal fluid. Cross-sectional OCT and angiography (OCTA) scans were used for training and testing ReF-Net. The effect of including OCTA data for retinal fluid segmentation was investigated in this study. Volumetric retinal fluid can be constructed using the output of ReF-Net. Area-under-receiver-operating-characteristic-curve, intersection-over-union (IoU), and F1-score were calculated to evaluate the performance of ReF-Net., Results: ReF-Net shows high accuracy (F1 = 0.864 ± 0.084) in retinal fluid segmentation. The performance can be further improved (F1 = 0.892 ± 0.038) by including information from both OCTA and structural OCT. ReF-Net also shows strong robustness to shadow artifacts. Volumetric retinal fluid can provide more comprehensive information than the two-dimensional (2D) area, whether cross-sectional or en face projections., Conclusions: A deep-learning-based method can accurately segment retinal fluid volumetrically on OCT/OCTA scans with strong robustness to shadow artifacts. OCTA data can improve retinal fluid segmentation. Volumetric representations of retinal fluid are superior to 2D projections., Translational Relevance: Using a deep learning method to segment retinal fluid volumetrically has the potential to improve the diagnostic accuracy of diabetic macular edema by OCT systems., Competing Interests: Disclosure: Y. Guo, None; T.T. Hormel, None; H. Xiong, None; J. Wang, None; T.S. Hwang, None; Y. Jia, Optovue (F, P), (Copyright 2020 The Authors.)

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., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Importance: Diabetic retinopathy (DR) is a leading cause of vision loss that is managed primarily through qualitative clinical examination of the retina. Optical coherence tomography angiography (OCTA) may offer an objective and quantitative method of evaluating DR., Objective: To quantify capillary nonperfusion in 3 vascular plexuses in the macula of eyes patients with diabetes of various retinopathy severity using projection-resolved OCTA (PR-OCTA)., Design, Setting, and Participants: Cross-sectional study at a tertiary academic center comprising 1 eye each from healthy control individuals and patients with diabetes at different severity stages of retinopathy. Data were acquired and analyzed between January 2015 and December 2017., Main Outcomes and Measures: Foveal avascular zone area, extrafoveal avascular area (EAA), and the sensitivity of detecting levels of retinopathy., Results: The study included 39 control individuals (20 women [51%]; mean [SD] age, 43.41 [19.37] years); 16 patients with diabetes without retinopathy (8 women [50%]; mean [SD] age, 56.50 [12.43] years); 23 patients with mild to moderate nonproliferative DR (18 women [78%] ; mean [SD] age, 62.48 [10.55] years); and 32 patients with severe nonproliferative DR or proliferative DR (12 women [38%]; mean age, 53.41 [14.05] years). Mean (SD) foveal avascular zone area was 0.203 (0.103) mm2 for control individuals, 0.192 (0.084) mm2 for patients with diabetes without retinopathy, 0.243 [0.079] mm2 for mild to moderate nonproliferative DR, and 0.359 (0.275) mm2 for severe nonproliferative DR or proliferative DR. Mean (SD) EAA in whole inner retinal slab in these groups, respectively, were 0.020 (0.031) mm2, 0.034 (0.047) mm2, 0.038 (0.040) mm2, and 0.237 (0.235) mm2. The mean (SD) sum of EAA from 3 segmented plexuses in each of the respective groups were 0.103 (0.169) mm2, 0.213 (0.242) mm2, 0.451 (0.243) mm2, and 1.325 (1.140) mm2. With specificity fixed at 95%, using EAA in inner retinal slab, the sensitivity of detecting patients with diabetes from healthy control individuals was 28% (95% CI, 18%-40%), 31% for patients with DR (95% CI, 19%-45%), and 47% for patients with severe DR (95% CI, 29%-65%) from whole inner retinal EAA. With the sum of EAA from 3 individual plexuses, the sensitivities were 69% (95% CI, 57%-80%), 82% (95% CI, 70%-91%), and 97% (95% CI, 85%-100%), respectively. Avascular areas were not associated with signal strength index. The commercial vessel density from the 2-plexus scheme distinguished the groups with lower sensitivity and were dependent on SSI., Conclusions and Relevance: Automatically quantified avascular areas from a 3-layer segmentation scheme using PR-OCTA distinguished levels of retinopathy with a greater sensitivity than avascular areas from unsegmented inner retinal slab or measurements from a commercially available vessel density in 2-layer scheme. Additional studies are needed to investigate the applicability of nonperfusion parameters in clinical settings.

Aim: To assess total retinal blood flow (TRBF) in diabetic retinopathy (DR) using multiplane en face Doppler optical coherence tomography (OCT)., Methods: A 70 kHz spectral-domain OCT system scanned a 2×2 mm area centred at the optic disc of the eyes with DR and healthy participants. The multiplane en face Doppler OCT algorithm generated a three-dimensional volumetric data set consisting of 195 en face planes. The TRBF was calculated from the maximum flow values of each branching retinal vein at an optimised en face plane. DR severity was graded according to the international clinical classification system. The generalised linear model method was used to compare flow values between DR groups and the control group., Results: A total of 71 eyes from 71 participants were included. Ten eyes were excluded due to poor image quality. The within-visit repeatability of scans was 4.1% (coefficient of variation). There was no significant difference in the TRBF between the healthy (46.7±10.2 µL/min) and mild/moderate non-proliferative DR (44.9±12.6 µL/min) groups. The TRBF in severe non-proliferative DR (39.1±12.6 µL/min) and proliferative DR (28.9±8.85 µL/min) groups were significantly lower ( p =0.04 and p <0.0001, respectively) than that of the healthy group. TRBF was correlated with DR disease severity ( p <0.0001, linear trend test)., Conclusion: The novel multiplane en face Doppler OCT method provided reliable measurements of TRBF in DR eyes. This may be a useful tool in understanding the pathophysiology of DR., Competing Interests: Competing interests: Oregon Health & Science University (OHSU), OT, DH and YJ have a significant financial interest in Optovue, a company that may have a commercial interest in the results of this research and technology. OT and DH have a significant financial interest in Carl Zeiss Meditec. These potential conflicts of interest have been reviewed and managed by OHSU., (© Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.)

Importance: Projection artifacts in optical coherence tomography angiography (OCTA) blur the retinal vascular plexuses together and limit visualization of the individual plexuses., Objective: To describe projection-resolved (PR) OCTA in eyes with diabetic retinopathy (DR) and healthy eyes., Design, Setting, and Participants: In this case-control study, patients with DR and healthy controls were enrolled in this observational study from January 26, 2015, to December 4, 2015, at a tertiary academic center. Spectral-domain, 70-kHz OCT obtained 3 × 3-mm macular scans. The PR algorithm suppressed projection artifacts. A semiautomated segmentation algorithm divided PR-OCTA into superficial, intermediate, and deep retinal plexuses. Two masked graders examined 3-layer PR-OCTA and combined angiograms for nonperfusion and abnormal capillaries., Main Outcomes and Measures: Retinal nonperfusion and capillary abnormalities and the diagnostic accuracy of detecting DR., Results: Twenty-nine eyes of 15 healthy individuals (mean [SD] age, 36.2 [13.4] years; 11 women) and 47 eyes of 29 patients with DR (mean [SD] age, 55.5 [11.9]; 10 women) underwent imaging. PR-OCTA revealed 3 distinct retinal plexuses in their known anatomical locations in all eyes. The intermediate and deep plexuses of healthy eyes revealed capillary networks of uniform density and caliber, whereas the superficial plexus revealed vessels in the familiar centripetal branching pattern. In eyes with DR, 3-layer PR-OCTA disclosed incongruent areas of nonperfusion and varied vessel caliber and density in the deeper plexuses. Masked grading of capillary nonperfusion on 3-layer PR-OCTA detected DR with 100% sensitivity (95% CI, 90.8%-100%) and 100% specificity (95% CI, 85.4%-100%). With unsegmented retinal angiograms, the sensitivity and specificity were 78.7% (95% CI, 63.9%-88.8%) and 100% (95% CI, 85.4%-100%), respectively (P = .002 for sensitivity). On 3-layer PR-OCTA, sensitivity was 72.2% (95% CI, 54.6%-85.2%) for severe nonproliferative DR and proliferative DR eyes with generalized nonperfusion in 2 or more individual plexuses, but on combined angiogram, sensitivity was 25.0% (95% CI, 12.7%-42.5%) for generalized nonperfusion (P < .001). PR-OCTA disclosed dilated vessels in the intermediate and deep plexuses in 23 eyes (100%) with proliferative DR, 13 eyes (100%) with severe nonproliferative DR, 8 eyes (73%) with mild to moderate nonproliferative DR, and 0 control eyes., Conclusions and Relevance: By presenting 3 retinal vascular plexuses distinctly, PR-OCTA reveals capillary abnormalities in deeper layers with clarity and may distinguish DR from healthy eyes and severe DR from mild DR with greater accuracy compared with conventional OCTA.

Purpose: The purpose of this study was to evaluate an automated algorithm for detecting avascular area (AA) in optical coherence tomography angiograms (OCTAs) separated into three individual plexuses using a projection-resolved technique., Methods: A 3 × 3 mm macular OCTA was obtained in 13 healthy and 13 mild nonproliferative diabetic retinopathy (NPDR) participants. A projection-resolved algorithm segmented OCTA into three vascular plexuses: superficial, intermediate, and deep. An automated algorithm detected AA in each of the three plexuses that were segmented and in the combined inner-retinal angiograms. We assessed the diagnostic accuracy of extrafoveal and total AA using segmented and combined angiograms, the agreement between automated and manual detection of AA, and the within-visit repeatability., Results: The sum of extrafoveal AA from the segmented angiograms was larger in the NPDR group by 0.17 mm2 (P < 0.001) and detected NPDR with 94.6% sensitivity (area under the receiver operating characteristic curve [AROC] = 0.99). In the combined inner-retinal angiograms, the extrafoveal AA was larger in the NPDR group by 0.01 mm2 (P = 0.168) and detected NPDR with 26.9% sensitivity (AROC = 0.62). The total AA, inclusive of the foveal avascular zone, in the segmented and combined angiograms, detected NPDR with 23.1% and 7.7% sensitivity, respectively. The agreement between the manual and automated detection of AA had a Jaccard index of >0.8. The pooled SDs of AA were small compared with the difference in mean for control and NPDR groups., Conclusions: An algorithm to detect AA in OCTA separated into three individual plexuses using a projection-resolved algorithm accurately distinguishes mild NPDR from control eyes. Automatically detected AA agrees with manual delineation and is highly repeatable.

Importance: Macular ischemia is a key feature of diabetic retinopathy (DR). Quantification of macular ischemia has potential as a biomarker for DR., Objective: To assess the feasibility of automated quantification of capillary nonperfusion as a potential sign of macular ischemia using optical coherence tomography (OCT) angiography., Design, Setting, and Participants: An observational study conducted in a tertiary, subspecialty, academic practice evaluated macular nonperfusion with 6 × 6-mm OCT angiography obtained with commercially available 70-kHz OCT and fluorescein angiography (FA). The study was conducted from January 22 to September 18, 2014. Data analysis was performed from October 1, 2014, to April 7, 2015. Participants included 12 individuals with normal vision serving as controls and 12 patients with various levels of DR., Main Outcomes and Measures: Preplanned primary measures were parafoveal and perifoveal vessel density, total avascular area, and foveal avascular zone as detected with 6 × 6-mm OCT angiography and analyzed using an automated algorithm. Secondary measures included the agreement of the avascular area between the OCT angiogram and FA., Results: Compared with the 12 healthy controls (11 women; mean [SD] age, 54.2 [14.2] years), the 12 participants with DR (4 women; mean [SD] age, 55.1 [12.1] years) had reduced parafoveal and perifoveal vessel density by 12.6% (95% CI, 7.7%-17.5%; P < .001) and 10.4% (95% CI, 6.8%-14.1%; P < .001), respectively. Total avascular area and foveal avascular zone area were greater in eyes with DR by 0.82 mm2 (95% CI, 0.65-0.99 mm2; P = .02) and 0.16 mm2 (95% CI, 0.05-0.28 mm2; P < .001). The agreement between the vascular areas in the OCT angiogram and FA had a κ value of 0.45 (95% CI, 0.21-0.70; P < .001). Total avascular area in the central 5.5-mm-diameter area distinguished eyes with DR from control eyes with 100% sensitivity and specificity., Conclusions and Relevance: Avascular area analysis with an automated algorithm using OCT angiography, although not equivalent to FA, detected DR reliably in this small pilot study. Further study is necessary to determine the usefulness of the automated quantification in clinical practice.

Purpose: To describe the optical coherence tomography angiography features of diabetic retinopathy., Methods: Using a 70 kHz optical coherence tomography and the split-spectrum amplitude decorrelation angiography algorithm, 6 mm × 6 mm 3-dimensional angiograms of the macula of 4 patients with diabetic retinopathy were obtained and compared with fluorescein angiography for features cataloged by the Early Treatment of Diabetic Retinopathy Study., Results: Optical coherence tomography angiography detected enlargement and distortion of the foveal avascular zone, retinal capillary dropout, and pruning of arteriolar branches. Areas of capillary loss obscured by fluorescein leakage on fluorescein angiography were more clearly defined on optical coherence tomography angiography. Some areas of focal leakage on fluorescein angiography that were thought to be microaneurysms were found to be small tufts of neovascularization that extended above the inner limiting membrane., Conclusion: Optical coherence tomography angiography does not show leakage but can better delineate areas of capillary dropout and detect early retinal neovascularization. This new noninvasive angiography technology may be useful for routine surveillance of proliferative and ischemic changes in diabetic retinopathy.