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To evaluate intraretinal cystoid spaces in patients with idiopathic macular hole (MH).Retrospective cohort study included consecutive patients with full-thickness MH who underwent successful MH surgery and 12 months of follow-up. Custom software was applied to preoperative optical coherence tomography scans to generate fluid volume. Inner fluid volume was defined as cystoid spaces in the inner nuclear layer, and outer fluid volume was defined as cystoid spaces in Henle fiber layer of the outer nuclear layer.Thirty-nine eyes from 39 participants were included. Postoperative 12-month visual acuity correlated with both inner fluid volume and minimum MH size (both P0.05) but not outer fluid volume. Inner fluid volume positively correlated with minimum MH size ( P = 0.0003). After accounting for minimum MH size with multivariable analysis, inner fluid volume effect on VA remained significant ( P = 0.025). After dividing inner fluid volume into tertiles, mean baseline visual acuity was 20/50 in eyes with small inner fluid volume, and was 20/125 in eyes with large inner fluid volume ( P = 0.0039). Mean postoperative 12-month visual acuity was 20/20 in eyes with small inner fluid volume compared with 20/32 in eyes with large inner fluid volume ( P = 0.019).Increased inner fluid volume was associated with worse postoperative VA.

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.

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

SynopsisA 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).AimsTo 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.MethodsThe 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.ResultsThe 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 pConclusionMacular 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.

A limitation of conventional optical coherence tomography angiography (OCTA) is the limited field of view normally used in data acquisition. As the technology improves, larger fields of view that capture information away from the macular are being explored in order to provide an enhanced ability to detect pathology. However, normative measurements for important OCTA metrics like vessel density and intercapillary distance are not currently well-characterized in the peripheral retina. In this prospective study, we measured vessel density and intercapillary distance of the superficial vascular complex, ganglion cell layer plexus, and deep capillary plexus in montaged macular/temporal scans from 53 (33 men) healthy volunteers. Vessel density and intercapillary distance were also compared across different regions of the retina, including along arcs at separate distance from the fovea. Compared to the central macular region, the temporal retina had significantly lower vessel density, decreased thickness, and greater intercapillary distance in the superficial vascular complex, GCLP ganglion cell layer plexus, and deep capillary plexus (Wilcoxon rank sum test P

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.

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.

To propose a deep-learning-based method to differentiate arteries from veins in montaged widefield OCT angiography (OCTA).Cross-sectional study.A total of 232 participants, including 109 participants with diabetic retinopathy (DR), 64 participants with branch retinal vein occlusion (BRVO), 27 participants with diabetes but without DR, and 32 healthy participants.We propose a convolutional neural network (CAVnet) to classify retinal blood vessels on montaged widefield OCTA en face images as arteries and veins. A total of 240 retinal angiograms from 88 eyes were used to train CAVnet, and 302 retinal angiograms from 144 eyes were used for testing. This method takes the OCTA images as input and outputs the segmentation results with arteries and veins down to the level of precapillary arterioles and postcapillary venules. The network also identifies their intersections. We evaluated the agreement (in pixels) between segmentation results and the manually graded ground truth using sensitivity, specificity, F1-score, and Intersection over Union (IoU). Measurements of arterial and venous caliber or tortuosity are made on our algorithm's output of healthy and diseased eyes.Classification of arteries and veins, arterial and venous caliber, and arterial and venous tortuosity.For classification and identification of arteries, the algorithm achieved average sensitivity of 95.3%, specificity of 99.6%, F1 score of 94.2%, and IoU of 89.3%. For veins, the algorithm achieved average sensitivity of 94.4%, specificity of 99.7%, F1 score of 94.1%, and IoU of 89.2%. We also achieved an average sensitivity of 76.3% in identifying intersection points. The results show CAVnet has high accuracy on differentiating arteries and veins in DR and BRVO cases. These classification results are robust across 2 instruments and multiple scan volume sizes. Outputs of CAVnet were used to measure arterial and venous caliber or tortuosity, and pixel-wise caliber and tortuosity maps were generated. Differences between healthy and diseased eyes were demonstrated, indicating potential clinical utility.The CAVnet can classify arteries and veins and their branches with high accuracy and is potentially useful in the analysis of vessel type-specific features on diseases such as branch retinal artery occlusion and BRVO.

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 inner nuclear layer (INL) may correlate more strongly with VA. DESIGN: Retrospective, cross-sectional study METHOD: One eye each of diabetic patients with DME were included. We measured intraretinal fluid volume that 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 inner nuclear layer (INL), and outer FV, the fluid between outer border of INL to ellipsoid zone. RESULT: We enrolled 125 patients with DME (60 women, mean 61-year-old). 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

PURPOSE: To compare retinal vessel density in eyes with geographic atrophy (GA) secondary to age-related macular degeneration (AMD) to age-matched healthy eyes using projection-resolved optical coherence tomography angiography (PR-OCTA). DESIGN: Prospective cross-sectional study. METHODS: Study participants underwent macular 3×3-mm OCTA scans with spectral domain OCTA. Reflectance-compensated retinal vessel densities were calculated on projection-resolved superficial vascular complex (SVC), intermediate capillary plexus (ICP), and deep capillary plexus (DCP). Quantitative analysis using normalized deviation compared the retinal vessel density in GA regions, 500 μm GA rim regions, and non-GA regions to similar macular locations in control eyes. RESULTS: Ten eyes with GA and 10 control eyes were studied. Eyes with GA had significantly lower vessel density in the SVC (54.8±2.4% vs. 60.8±3.1%, P

Purpose To detect nonexudative choroidal neovascularization (CNV) in age-related macular degeneration (AMD) with OCT angiography (OCTA) and determine the risk of exudative CNV developing compared with eyes without nonexudative CNV. Design Prospective, longitudinal, observational study. Participants Consecutive patients with drusen and pigmentary changes in the study eye and exudative neovascular AMD in the fellow eye. Methods In this prospective observational study, participants underwent spectral-domain OCTA (AngioVue; Optovue, Inc, Fremont, CA), clinical examination, and structural OCT at baseline and 6-month intervals for 2 years. OCT angiography images were exported for custom processing to remove projection artifact and calculate CNV vessel area. Main Outcome Measures Rate of developing exudation in eyes with and without nonexudative CNV as detected by OCTA on regular follow-up. Results Sixty-three study participants were followed up every 6 months and 48 completed the 2-year study. Mean age was 78 years and 60.3% were female. On the baseline visit, 5 eyes (7.9%) were found to have nonexudative CNV by OCTA, and 3 of them demonstrated exudation. Of 58 eyes with a normal OCTA on baseline visit, 5 eyes developed nonexudative CNV during a follow-up visit. All 5 of these nonexudative CNV went on to develop exudation in subsequent visits. Overall, 8 of the 10 eyes with nonexudative CNV developed exudation with a mean time of 8 months and mean CNV area growth rate of 20% per month (P = 0.014, exponential model). Initiation of antiangiogenic treatment halted their growth. In comparison, exudation occurred in only 6 of the 53 eyes (11%) that lacked a precursor nonexudative CNV. Cox proportional hazard analysis showed that having nonexudative CNV detected was associated with an 18.1-fold increase in the rate of exudation subsequently developing (P Conclusions Nonexudative CNV frequently is detected by OCTA in the fellow eyes of those with exudative CNV. These lesions carry a high risk of exudation developing within the first year after detection and could benefit from close monitoring. The high risk of progression may justify prophylactic treatment; further studies are needed.

Purpose To investigate ophthalmologists’ rate of attestation to meaningful use (MU) of their electronic health record (EHR) systems in the Medicare EHR Incentive Program and their continuity and success in receiving payments in comparison with other specialties. Design Administrative database study. Participants Eligible professionals participating in the Medicare EHR Incentive Program. Methods Based on publicly available data sources, subsets of payment and attestation data were created for ophthalmologists and for other specialties. The number of eligible professionals attesting was determined using the attestation data for each year and stage of the program. The proportion of attestations by EHR vendor was calculated using all attestations for each vendor. Main Outcome Measures Numbers of ophthalmologists attesting by year and stage of the Medicare EHR Incentive Program, incentive payments, and number of attestations by EHR vendor. Results In the peak year of participation, 51.6% of ophthalmologists successfully attested to MU, compared with 37.1% of optometrists, 50.2% of dermatologists, 54.5% of otolaryngologists, and 64.4% of urologists. Across the 6 years of the program, ophthalmologists received an average of $17 942 in incentive payments compared with $11 105 for optometrists, $16 617 for dermatologists, $20 203 for otolaryngologists, and $23 821 for urologists. Epic and Nextgen were the most frequently used EHRs for attestation by ophthalmologists. Conclusions Ophthalmology as a specialty performed better than optometry and dermatology, but worse than otolaryngology and urology, in terms of the proportion of eligible professionals attesting to MU of EHRs. Ophthalmologists were more likely to remain in the program after their initial year of attestation compared with all eligible providers. The top 4 EHR vendors accounted for 50% of attestations by ophthalmologists.

Optical coherence tomography (OCT) is widely used in ophthalmic practice because it can visualize retinal structure and vasculature in vivo and 3-dimensionally (3D). Even though OCT procedures yield data volumes, clinicians typically interpret the 3D images using two-dimensional (2D) data subsets, such as cross-sectional scans or en face projections. Since a single OCT volume can contain hundreds of cross-sections (each of which must be processed with retinal layer segmentation to produce en face images), a thorough manual analysis of the complete OCT volume can be prohibitively time-consuming. Furthermore, 2D reductions of the full OCT volume may obscure relationships between disease progression and the (volumetric) location of pathology within the retina and can be prone to mis-segmentation artifacts. In this work, we propose a novel framework that can detect several retinal pathologies in three dimensions using structural and angiographic OCT. Our framework operates by detecting deviations in reflectance, angiography, and simulated perfusion from a percent depth normalized standard retina created by merging and averaging scans from healthy subjects. We show that these deviations from the standard retina can highlight multiple key features, while the depth normalization obviates the need to segment several retinal layers. We also construct a composite pathology index that measures average deviation from the standard retina in several categories (hypo- and hyper-reflectance, nonperfusion, presence of choroidal neovascularization, and thickness change) and show that this index correlates with DR severity. Requiring minimal retinal layer segmentation and being fully automated, this 3D framework has a strong potential to be integrated into commercial OCT systems and to benefit ophthalmology research and clinical care.

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

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.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.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.A deep learning framework only based on OCT and OCTA can provide specialist-level DR classification using only a single imaging modality.The proposed framework can be used to develop clinically valuable automated DR diagnosis system because of the specialist-level performance showed in this study.

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 from 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.

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.

ObjectiveTo 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 analysisIn 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.ResultsNineteen 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).ConclusionPR-OCTA showed that EAMD is associated with focal avascular area in all the three retinal vascular plexuses.

PURPOSE Stickler syndrome is associated with open-angle glaucoma, but no reports exist of adults presenting with acute angle closure. Here, we report the clinical associations and short-term outcomes in 2 patients with Stickler syndrome who presented with acute angle closure at a single tertiary referral center. OBSERVATIONS A 36-year-old female with Stickler syndrome presented with acute onset of red and painful left eye with cloudy vision. Initial intraocular pressure (IOP) was measured to be 54 mm Hg. Gonioscopy revealed no angle structures or peripheral anterior synechiae in the left eye. Two laser peripheral iridotomies were created 5 days apart without adequate IOP lowering. Left eye cataract extraction with intraocular lens implantation and Ahmed glaucoma drainage device implantation were performed with resolution of ocular hypertension.A 35-year-old male with Stickler syndrome presented with intermittent right eye pain and blurry vision. IOP was 42 mm Hg and gonioscopy revealed 270 degrees of angle closure in the right eye. The patient underwent laser peripheral iridotomy in the right eye with resolution of ocular hypertension by the next day. CONCLUSIONS Recognizing angle closure as a potential feature of Stickler syndrome can help providers adequately screen and manage patients with Stickler syndrome presenting with acutely elevated IOP.

Typical optical coherence tomographic angiography (OCTA) acquisition areas on commercial devices are 3×3- or 6×6-mm. Compared to 3×3-mm angiograms with proper sampling density, 6×6-mm angiograms have significantly lower scan quality, with reduced signal-to-noise ratio and worse shadow artifacts due to undersampling. Here, we propose a deep-learning-based high-resolution angiogram reconstruction network (HARNet) to generate enhanced 6×6-mm superficial vascular complex (SVC) angiograms. The network was trained on data from 3×3-mm and 6×6-mm angiograms from the same eyes. The reconstructed 6×6-mm angiograms have significantly lower noise intensity, stronger contrast and better vascular connectivity than the original images. The algorithm did not generate false flow signal at the noise level presented by the original angiograms. The image enhancement produced by our algorithm may improve biomarker measurements and qualitative clinical assessment of 6×6-mm OCTA.

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.

Purpose To use OCT angiography (OCTA)-derived quantitative metrics to assess the response of choroidal neovascularization (CNV) to pro re nata (PRN) anti–vascular endothelial growth factor (VEGF) treatment in neovascular age-related macular degeneration (AMD). Design Prospective, longitudinal cohort study. Participants Fourteen eyes from 14 study participants with treatment-naive neovascular AMD were enrolled. Methods Participants were evaluated monthly and treated with intravitreal anti-VEGF agents under a PRN protocol for 1 year. At each visit, two 3 × 3-mm2 OCTA scans were obtained. Custom image processing was applied to segment the outer retinal slab, suppress projection artifact, and automatically detect CNV. Choroidal neovascularization membrane area and CNV vessel area were calculated. Main Outcome Measures Individual and mean CNV membrane area and CNV vessel area at each visit; within-visit repeatability determined by coefficient of variation. Results Eight eyes showed the entire CNV to be within the 3 × 3-mm2 scanning area and had adequate image quality for CNV quantification. One patient (patient 2) was excluded from analysis because of the presence of a large subretinal hemorrhage overlying the CNV membrane. In the remaining patients, CNV vessel area was reduced by 39%, 50%, 43%, and 41% at months 1, 3, 6, and 12, respectively. Choroidal neovascularization membrane area was reduced by 39%, 51%, 54%, and 45% at months 1, 3, 6, and 12. At month 6, mean change from baseline was not statistically significant for CNV vessel area, whereas it was statistically significant for CNV membrane area. Neither metric was significantly different compared with baseline at month 12. Individual analyses revealed each CNV had a unique response under PRN treatment. Within-visit repeatability was 7.96% (coefficient of variation) for CNV vessel area and 7.37% for CNV membrane area. Conclusions In this small exploratory study of CNV response to PRN anti-VEGF treatment, both CNV vessel area and membrane area were reduced compared with baseline after 3 months. After 1 year of follow-up, these reductions were no longer statistically significant. When anti-VEGF treatment was withheld, increasing CNV vessel area over time often resulted in exudation, but it was not possible to determine exactly when exudation occurs.

Purpose: The purpose of this study was to compare rates of clinically significant complications between 23- and 25-gauge pars plana vitrectomy (PPV) in vitreoretinal surgery. Methods: Demographics, PPV indication, and surgical complications were reviewed. Patients with prior PPV or other retina surgery; cases requiring silicone oil removal, keratoplasty, or glaucoma valve implant; patients Results: A total of 579 eyes met inclusion criteria, and their charts were reviewed. Demographics, PPV indication, follow-up time, and lens status were similar ( P > .05). A 23-gauge PPV was performed more frequently than a 25-gauge PPV (328 vs 251 eyes, respectively). Although rates of eyes with a clinically significant postoperative complication requiring surgical intervention were higher in 23-gauge PPV (112/325, 34.4%) than in 25-gauge PPV (54/250, 21.6%), PPVs indicated by rhegmatogenous retinal detachment were more common with 23-gauge PPVs (155/325, 47.7%) than with 25-gauge PPVs (37/250, 14.8%; P Conclusions: This retrospective review suggests that clinically significant complications are moderately more likely following 23-gauge PPV compared with 25-gauge PPV, even when the differences in surgical indication are considered.

Purpose The purpose of this study was to assess the associations between baseline choriocapillaris (CC) flow deficits and geographic atrophy (GA) progression. Methods In this prospective cohort study, patients with GA underwent 3 × 3-mm macular spectral-domain optical coherence tomographic angiography (OCTA) at baseline and follow-up visits. Annual GA enlargement rate was defined as change of square root of GA area in 12 months. Shadow areas due to iris, media opacity, retinal vessels, and drusen were excluded. CC vessel density (CC-VD) in non-GA areas was measured using a validated machine-learning-based algorithm. Low perfusion area (LPA) was defined as capillary density below the 0.1 percentile threshold of the same location of 40 normal healthy control eye. Focal perfusion loss (FPL) was defined as percentage of CC loss within LPA compared with normal controls. Results Ten patients with GA were enrolled and followed for 26 months on average. At baseline, the mean GA area was 0.84 ± 0.70 mm2. The mean CC-VD was 44.5 ± 15.2%, the mean LPA was 4.29 ± 2.6 mm2, and the mean FPL was 50.4 ± 28.2%. The annual GA enlargement rate was significantly associated with baseline CC-VD (r = −0.816, P = 0.004), LPA (r = 0.809, P = 0.005), and FPL (r = 0.800, P = 0.005), but not with age (r = 0.008, P = 0.98) and GA area (r = −0.362, P = 0.30). Conclusions Baseline CC flow deficits were significantly associated with a faster GA enlargement over the course of 1 year, suggesting the choriocapillaris perfusion outside of a GA area may play a role in GA progression.

Purpose To describe an automated algorithm to quantify the foveal avascular zone (FAZ), using optical coherence tomography angiography (OCTA), and to compare its performance for diagnosis of diabetic retinopathy (DR) and association with best-corrected visual acuity (BCVA) to that of extrafoveal avascular area (EAA). Methods We obtained 3 × 3-mm macular OCTA scans in diabetic patients with various levels of DR and healthy controls. An algorithm based on a generalized gradient vector flow (GGVF) snake model detected the FAZ, and metrics assessing FAZ size and irregularity were calculated. We compared the automated FAZ segmentation to manual delineation and tested the within-visit repeatability of FAZ metrics. The correlations of two conventional FAZ metrics, two novel FAZ metrics, and EAA with DR severity and BCVA, as determined by Early Treatment Diabetic Retinopathy Study (ETDRS) charts, were assessed. Results Sixty-six eyes from 66 diabetic patients and 19 control eyes from 19 healthy participants were included. The agreement between manual and automated FAZ delineation had a Jaccard index > 0.82, and the repeatability of automated FAZ detection was excellent in eyes at all levels of DR severity. FAZ metrics that incorporated both FAZ size and shape irregularity had the strongest correlation with clinical DR grade and BCVA. Of all the tested OCTA metrics, EAA had the greatest sensitivity in differentiating diabetic eyes without clinical evidence of retinopathy, mild to moderate nonproliferative DR (NPDR), and severe NPDR to proliferative DR from healthy controls. Conclusions The GGVF snake algorithm tested in this study can accurately and reliably detect the FAZ, using OCTA data at all DR severity grades, and may be used to obtain clinically useful information from OCTA data regarding macular ischemia in patients with diabetes. While FAZ metrics can provide clinically useful information regarding macular ischemia, and possibly visual acuity potential, EAA measurements may be a better biomarker for DR.

Purpose To determine the sensitivity and specificity of OCT angiography (OCTA) in the detection of choroidal neovascularization (CNV) in age-related macular degeneration (AMD). Design Prospective case series. Subjects A prospective series of 72 eyes were studied, which included eyes with treatment-naive CNV due to AMD, non-neovascular AMD, and normal controls. Methods All eyes underwent OCTA with a spectral domain OCT. The 3-dimensional angiogram was segmented into separate en face views including the inner retinal angiogram, outer retinal angiogram, and choriocapillaris angiogram. Detection of abnormal flow in the outer retina served as candidate CNV with OCTA. Masked graders reviewed structural OCT alone, en face OCTA alone, and en face OCTA combined with cross-sectional OCTA for the presence of CNV. Main Outcome Measure The sensitivity and specificity of CNV detection compared to the gold standard of fluorescein angiography and OCT was determined for structural spectral domain OCT alone, en face OCTA alone, and with en face OCTA combined with cross-sectional OCTA. Results Of 32 eyes with CNV, both graders identified 26 true positives with en face OCTA alone, resulting in a sensitivity of 81.3%. Four of the 6 false negatives had large subretinal hemorrhage and sensitivity improved to 94% for both graders if eyes with subretinal hemorrhage were excluded. The addition of cross-sectional OCTA along with en face OCTA improved the sensitivity to 100% for both graders. Structural OCT alone also had a sensitivity of 100%. The specificity of en face OCTA alone was 92.5% for grader A and 97.5% for grader B. The specificity of structural OCT alone was 97.5% for grader A and 85% for grader B. Cross-sectional OCTA combined with en face OCTA had a specificity of 97.5% for grader A and 100% for grader B. Conclusions Sensitivity and specificity for CNV detection with en face OCTA combined with cross-sectional OCTA approaches that of the gold standard of fluorescein angiography with OCT, and it is better than en face OCTA alone. Structural OCT alone has excellent sensitivity for CNV detection. False positives from structural OCT can be mitigated with the addition of flow information with OCTA.

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

In this study, we demonstrate a novel self-navigated motion correction method that suppresses eye motion and blinking artifacts on wide-field optical coherence tomographic angiography (OCTA) without requiring any hardware modification. Highly efficient GPU-based, real-time OCTA image acquisition and processing software was developed to detect eye motion artifacts. The algorithm includes an instantaneous motion index that evaluates the strength of motion artifact on en face OCTA images. Areas with suprathreshold motion and eye blinking artifacts are automatically rescanned in real-time. Both healthy eyes and eyes with diabetic retinopathy were imaged, and the self-navigated motion correction performance was demonstrated.

Purpose Thinning of the inner layers of the retina occurs in patients with central retinal artery occlusion (CRAO). The mechanism for such thinning may be partially due to proteolysis by a calcium-activated protease called calpain. Calpain inhibitor SNJ-1945 ameliorated the proteolysis in a past series of model experiments. The purposes of the present retrospective study were to: 1) use segmentation analysis of optical coherence tomography (OCT) images to mathematically model the loss of specific retinal layers in CRAO patients, and 2) predict the number of patients and days of observation needed for clinical trials of inhibitors against CRAO. Methods A retrospective case control study was conducted by computer-aided search for CRAO (ICD10 H43.1) with at least one OCT procedure (CPT: 92134) in the OHSU Epic patient data base. Results After initial swelling, thinning of the inner retinal layers, especially the ganglion cell (GCL) layer followed exponential decay curves. Using sample size statistics and GCL thickness as a marker in a 30-day clinical trial, 19 eyes/group could theoretically detect a 20% beneficial effect of an inhibitor against CRAO. Other markers, such as the whole retinal thickness and combined inner layers could also be used as less-specific markers. Conclusions Using thickness changes in the GCL layer to monitor the efficacy of potential inhibitors against CRAO is practical in human trials requiring a reasonable number of patients and relatively short trial period. Translational relevance Measurement of GCL thickness would be a useful indicator of CRAO progression in a clinical trial of putative inhibitors.

The capillary nonperfusion area (NPA) is a key quantifiable biomarker in the evaluation of diabetic retinopathy (DR) using optical coherence tomography angiography (OCTA). However, signal reduction artifacts caused by vitreous floaters, pupil vignetting, or defocus present significant obstacles to accurate quantification. We have developed a convolutional neural network, MEDnet-V2, to distinguish NPA from signal reduction artifacts in 6×6 mm(2) OCTA. The network achieves strong specificity and sensitivity for NPA detection across a wide range of DR severity and scan quality.

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.

Purpose To characterize the density and distribution of the radial peripapillary capillary plexus (RPCP) and its relationship with retinal nerve fiber layer (NFL) thickness in healthy subjects. Methods Using spectral-domain optical coherence tomography (OCT), split-spectrum amplitude decorrelation angiography algorithm and automated montaging, wide-field OCT angiography (OCTA) was used to measure the RPCP capillary density (CD) and NFL thickness. Polar sector-average CD and thickness maps were also created on each eye. Results Wide-field OCTA (8 × 8 mm) in 10 healthy eyes from 10 subjects demonstrated the distribution of the RPCP throughout the posterior pole. RPCP-CD decreases with distance from the disc, but along the arcuate nerve fiber bundles relatively dense (> half maximum density) RPCP extends more than 5 mm from the disc and includes regions superior to and inferior to the macula. The RPCP-CD and NFL thickness are highly correlated (R2 = 0.85, P < 0.001) and fit well with a nonlinear stacked-layer model. The model fit suggests that the RPCP is present when the NFL is thicker than 17.9 μm and reaches a ceiling area density of 84% and that the RPCP has an apparent volume density of 19% at the current instrument transverse resolution. This indicates that capillary overlap can be expected to occur when NFL thickness reaches 40 μm. Conclusions The wide distribution of dense overlapping RPCP suggests that wider (up to 8 mm vertical and 7 mm horizontal) OCTA scans may be better investigate capillary loss in the early stages of glaucoma or other optic neuropathies.