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

Purpose: To train and validate a convolutional neural network to segment nonperfusion areas (NPAs) in multiple retinal vascular plexuses on widefield optical coherence tomography angiography (OCTA). Methods: This cross-sectional study included 202 participants with a full range of diabetic retinopathy (DR) severities (diabetes mellitus without retinopathy, mild to moderate non-proliferative DR, severe non-proliferative DR, and proliferative DR) and 39 healthy participants. Consecutive 6 × 6-mm OCTA scans at the central macula, optic disc, and temporal region in one eye from 202 participants in a clinical DR study were acquired with a 70-kHz OCT commercial system (RTVue-XR). Widefield OCTA en face images were generated by montaging the scans from these three regions. A projection-resolved OCTA algorithm was applied to remove projection artifacts at the voxel scale. A deep convolutional neural network with a parallel U-Net module was designed to detect NPAs and distinguish signal reduction artifacts from flow deficits in the superficial vascular complex (SVC), intermediate capillary plexus (ICP), and deep capillary plexus (DCP). Expert graders manually labeled NPAs and signal reduction artifacts for the ground truth. Sixfold cross-validation was used to evaluate the proposed algorithm on the entire dataset. Results: The proposed algorithm showed high agreement with the manually delineated ground truth for NPA detection in three retinal vascular plexuses on widefield OCTA (mean ± SD F-score: SVC, 0.84 ± 0.05; ICP, 0.87 ± 0.04; DCP, 0.83 ± 0.07). The extrafoveal avascular area in the DCP showed the best sensitivity for differentiating eyes with diabetes but no retinopathy (77%) from healthy controls and for differentiating DR by severity: DR versus no DR, 77%; referable DR (rDR) versus non-referable DR (nrDR), 79%; vision-threatening DR (vtDR) versus non-vision-threatening DR (nvtDR), 60%. The DCP also showed the best area under the receiver operating characteristic curve for distinguishing diabetes from healthy controls (96%), DR versus no DR (95%), and rDR versus nrDR (96%). The three-plexus-combined OCTA achieved the best result in differentiating vtDR and nvtDR (81.0%). Conclusions: A deep learning network can accurately segment NPAs in individual retinal vascular plexuses and improve DR diagnostic accuracy. Translational Relevance: Using a deep learning method to segment nonperfusion areas in widefield OCTA can potentially improve the diagnostic accuracy of diabetic retinopathy by OCT/OCTA systems.

Perimacular Atrophy Following Voretigene Neparvovec-Rzyl Treatment in the Setting of Previous Contralateral Eye Treatment With a Different Viral Vector.

Purpose: To report on cases of unilateral perimacular atrophy after treatment with voretigene neparvovec-rzyl, in the setting of previous contralateral eye treatment with a different viral vector. Design: Single-center, retrospective chart review. Methods: In this case series, four patients between the ages of six and 11 years old with RPE65-related retinopathy were treated unilaterally with rAAV2-CB-hRPE65 as part of a gene augmentation clinical trial (NCT00749957). Six to 10 years later the contralateral eyes were treated with the Food and Drug Administration-approved drug, voretigene neparvovec-rzyl. Best-corrected visual acuity (BCVA), fundus photos, ocular coherence tomography, two-color dark-adapted perimetry, full field stimulus threshold testing (FST), and location of subretinal bleb and chorioretinal atrophy were evaluated. Results: Three out of four patients showed unilateral perimacular atrophy after treatment with voretigene, ranging from five to 22 months after treatment. Areas of robust visual field improvement were followed by areas of chorioretinal atrophy. Despite perimacular changes, BCVA, FST, and subjective improvements in vision and nyctalopia were maintained. Perimacular atrophy was not observed in the first eye treated with the previous viral vector. Conclusions: We observed areas of robust visual field improvement followed by perimacular atrophy in voretigene treated eyes, as compared to the initially treated contralateral eyes. Translational Relevance: Caution is advised when using two different viral vectors between eyes in gene therapy. This may become an important issue in the future with increasing gene therapy clinical trials for inherited retinal dystrophies.

Gene Editing for CEP290-Associated Retinal Degeneration.

BACKGROUND: CEP290-associated inherited retinal degeneration causes severe early-onset vision loss due to pathogenic variants in CEP290. EDIT-101 is a clustered regularly interspaced short palindromic repeats (CRISPR)-CRISPR-associated protein 9 (Cas9) gene-editing complex designed to treat inherited retinal degeneration caused by a specific damaging variant in intron 26 of CEP290 (IVS26 variant). METHODS: We performed a phase 1-2, open-label, single-ascending-dose study in which persons 3 years of age or older with CEP290-associated inherited retinal degeneration caused by a homozygous or compound heterozygous IVS26 variant received a subretinal injection of EDIT-101 in the worse (study) eye. The primary outcome was safety, which included adverse events and dose-limiting toxic effects. Key secondary efficacy outcomes were the change from baseline in the best corrected visual acuity, the retinal sensitivity detected with the use of full-field stimulus testing (FST), the score on the Ora-Visual Navigation Challenge mobility test, and the vision-related quality-of-life score on the National Eye Institute Visual Function Questionnaire-25 (in adults) or the Children's Visual Function Questionnaire (in children). RESULTS: EDIT-101 was injected in 12 adults 17 to 63 years of age (median, 37 years) at a low dose (in 2 participants), an intermediate dose (in 5), or a high dose (in 5) and in 2 children 9 and 14 years of age at the intermediate dose. At baseline, the median best corrected visual acuity in the study eye was 2.4 log10 of the minimum angle of resolution (range, 3.9 to 0.6). No serious adverse events related to the treatment or procedure and no dose-limiting toxic effects were recorded. Six participants had a meaningful improvement from baseline in cone-mediated vision as assessed with the use of FST, of whom 5 had improvement in at least one other key secondary outcome. Nine participants (64%) had a meaningful improvement from baseline in the best corrected visual acuity, the sensitivity to red light as measured with FST, or the score on the mobility test. Six participants had a meaningful improvement from baseline in the vision-related quality-of-life score. CONCLUSIONS: The safety profile and improvements in photoreceptor function after EDIT-101 treatment in this small phase 1-2 study support further research of in vivo CRISPR-Cas9 gene editing to treat inherited retinal degenerations due to the IVS26 variant of CEP290 and other genetic causes. (Funded by Editas Medicine and others; BRILLIANCE ClinicalTrials.gov number, NCT03872479.).

Visualizing features with wide-field volumetric OCT angiography.

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

Interpretable Diabetic Retinopathy Diagnosis Based on Biomarker Activation Map.

OBJECTIVE: Deep learning classifiers provide the most accurate means of automatically diagnosing diabetic retinopathy (DR) based on optical coherence tomography (OCT) and its angiography (OCTA). The power of these models is attributable in part to the inclusion of hidden layers that provide the complexity required to achieve a desired task. However, hidden layers also render algorithm outputs difficult to interpret. Here we introduce a novel biomarker activation map (BAM) framework based on generative adversarial learning that allows clinicians to verify and understand classifiers' decision-making. METHODS: A data set including 456 macular scans were graded as non-referable or referable DR based on current clinical standards. A DR classifier that was used to evaluate our BAM was first trained based on this data set. The BAM generation framework was designed by combing two U-shaped generators to provide meaningful interpretability to this classifier. The main generator was trained to take referable scans as input and produce an output that would be classified by the classifier as non-referable. The BAM is then constructed as the difference image between the output and input of the main generator. To ensure that the BAM only highlights classifier-utilized biomarkers an assistant generator was trained to do the opposite, producing scans that would be classified as referable by the classifier from non-referable scans. RESULTS: The generated BAMs highlighted known pathologic features including nonperfusion area and retinal fluid. CONCLUSION/SIGNIFICANCE: A fully interpretable classifier based on these highlights could help clinicians better utilize and verify automated DR diagnosis.

Early Sign of Retinal Neovascularization Evolution in Diabetic Retinopathy: A Longitudinal OCT Angiography Study.

Purpose: To assess whether the combination of en face OCT and OCT angiography (OCTA) can capture observable, but subtle, structural changes that precede clinically evident retinal neovascularization (RNV) in eyes with diabetic retinopathy (DR). Design: Retrospective, longitudinal study. Participants: Patients with DR that had at least 2 visits. Methods: We obtained wide-field OCTA scans of 1 eye from each participant and generated en face OCT, en face OCTA, and cross-sectional OCTA. We identified eyes with RNV sprouts, defined as epiretinal hyperreflective materials on en face OCT with flow signals breaching the internal limiting membrane on the cross-sectional OCTA without recognizable RNV on en face OCTA and RNV fronds, defined as recognizable abnormal vascular structures on the en face OCTA. We examined the corresponding location from follow-up or previous visits for the presence or progression of the RNV. Main Outcome Measures: The characteristics and longitudinal observation of early signs of RNV. Results: From 71 eyes, we identified RNV in 20 eyes with the combination of OCT and OCTA, of which 13 (65%) were photographically graded as proliferative DR, 6 (30%) severe nonproliferative DR, and 1 (5%) moderate nonproliferative diabetic retinopathy. From these eyes, we identified 38 RNV sprouts and 26 RNV fronds at the baseline. Thirty-four RNVs (53%) originated from veins, 24 (38%) were from intraretinal microabnormalities, and 6 (9%) were from a nondilated capillary bed. At the final visit, 53 RNV sprouts and 30 RNV fronds were detected. Ten eyes (50%) showed progression, defined as having a new RNV lesion or the development of an RNV frond from an RNV sprout. Four (11%) RNV sprouts developed into RNV fronds with a mean interval of 7.0 months. Nineteen new RNV sprouts developed during the follow-up, whereas no new RNV frond was observed outside an identified RNV sprout. The eyes with progression were of younger age (P = 0.014) and tended to be treatment naive (P = 0.07) compared with eyes without progression. Conclusions: Longitudinal observation demonstrated that a combination of en face OCT and cross-sectional OCTA can identify an earlier form of RNV before it can be recognized on en face OCTA. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

PARACENTRAL ACUTE MIDDLE MACULOPATHY AS AN EARLY SIGN OF WALDENSTRÖM MACROGLOBULINEMIA.

PURPOSE: To report a case of paracentral acute middle maculopathy as the earliest sign of an undiagnosed, life-threatening hyperviscosity syndrome. METHODS: A 78-year-old man with an acute paracentral scotoma and examination findings of bilateral arteriolar tortuosity and unilateral paracentral acute middle maculopathy. RESULTS: Work-up revealed anemia and elevated serum viscosity. Protein electrophoresis demonstrated an immunoglobulin M kappa monoclonal protein spike, and bone marrow biopsy confirmed an immunoglobulin M gammopathy consistent with Waldenström macroglobulinemia. Systemic chemotherapy was initiated. CONCLUSION: This case demonstrates typical optical coherence tomography findings of paracentral acute middle maculopathy, which led to the diagnosis of a rare lymphoproliferative disorder. This highlights the importance of a prompt work-up for paracentral acute middle maculopathy to detect underlying systemic diseases, including hyperviscosity syndromes.

Automated Macular Fluid Volume As a Treatment Indicator for Diabetic Macular Edema.

Introduction: To assess the diagnostic accuracy of automatically quantified macular fluid volume (MFV) for treatment-required diabetic macular edema (DME). Methods: This retrospective cross-sectional study included eyes with DME. The commercial software on optical coherence tomography (OCT) produced the central subfield thickness (CST), and a custom deep-learning algorithm automatically segmented the fluid cysts and quantified the MFV from the volumetric scans of an OCT angiography system. Retina specialists treated patients per standard of care based on clinical and OCT findings without access to the MFV. The main outcome measures were the area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity of the CST, MFV, and visual acuity (VA) for treatment indication. Results: Of 139 eyes, 39 (28%) were treated for DME during the study period and 101 (72%) were previously treated. The algorithm detected fluid in all eyes; however, only 54 eyes (39%) met the DRCR.net criteria for center-involved ME. The AUROC of MFV predicting a treatment decision of 0.81 was greater than that of CST (0.67) (P = .0048). Untreated eyes that met the optimal threshold for treatment-required DME based on MFV (>0.031 mm3) had better VA than treated eyes (P = .0053). A multivariate logistic regression model showed that MFV (P = .0008) and VA (P = .0061) were significantly associated with a treatment decision, but CST was not. Conclusions: MFV had a higher correlation with the need for treatment for DME than CST and may be especially useful for ongoing management of DME.

Signal attenuation-compensated projection-resolved OCT angiography.

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

Improved Rod Sensitivity as Assessed by Two-Color Dark-Adapted Perimetry in Patients With RPE65-Related Retinopathy Treated With Voretigene Neparvovec-rzyl.

Purpose: The purpose of this study was to evaluate rod-mediated function with two-color dark-adapted perimetry (2cDAP) in patients with RPE65-related retinopathy treated with voretigene neparvovec-rzyl. Methods: Following dilation and dark adaptation, 2cDAP and FST were performed. The 2cDAP was measured on an Octopus 900 perimeter (Haag-Streit) with cyan (500 nm wavelength) and red (650 nm wavelength) stimuli. Hill of vision (HOV) analysis was performed on 2cDAP perimetry with Visual Field Modeling and Analysis (VFMA). Full field threshold stimulus testing (FST) was also measured as a secondary measure of rod-mediated function, and assessed on a Diagnosys Espion with the ColorDome stimulator (Diagnosys LLC). Results: Eight eyes from 4 patients who were treated with voretigene bilaterally had rod function assessed by 2cDAP testing at least 1 year after treatment. There was statistically significant improvement in 2cDAP following gene augmentation therapy. HOV VFMA analysis showed widespread improvements that extended beyond the treatment bleb and statistically significant improvement in HOV analysis volumetric measurements post-treatment to cyan and red stimuli. FST testing performed in six eyes from three patients demonstrated statistically significant improvement to all chromatic stimuli following treatment. Conclusions: These findings demonstrated statistically significant improvement in 2cDAP and FST following treatment with voretigene. Translational Relevance: These findings provide a sensitive method of assessing rod-mediated function in a topographic manner that may be useful in future clinical trials for inherited retinal dystrophies.

Deep Learning for Diagnosing and Segmenting Choroidal Neovascularization in OCT Angiography in a Large Real-World Data Set.

Purpose: To diagnose and segment choroidal neovascularization (CNV) in a real-world multicenter clinical OCT angiography (OCTA) data set using deep learning. Methods: A total of 105,66 OCTA scans from 3135 eyes, including 4701 with CNV and 5865 without, were collected in five eye clinics. Both 3 × 3-mm and 6 × 6-mm scans of the central and temporal macula were included. Scans with CNV were collected from multiple diseases, and scans without CNV were collected from both healthy controls and those with multiple diseases. No scans were removed during training or testing due to poor quality. The trained hybrid multitask convolutional neural network outputs a CNV diagnosis and membrane segmentation, respectively. Results: The model demonstrated a highly accurate CNV diagnosis (area under receiver operating characteristic curve = 0.97), achieving a sensitivity of 95% at 95% specificity. The model also correctly segmented CNV lesions (F1 score = 0.78 ± 0.19). Additionally, model performance was comparable on both high-definition 3 × 3-mm scans and low-definition 6 × 6-mm scans. The model did not suffer large performance variations under different diseases. We also show that a subclinical lesion in a patient with neovascular age-related macular degeneration can be monitored over a multiyear time frame using our approach. Conclusions: The proposed method can accurately diagnose and segment CNV in a large real-world clinical data set. Translational Relevance: The algorithm could enable automated CNV screening and quantification in the clinic, which will help improve CNV diagnosis and treatment evaluation.

Utility of En Face OCT for the Detection of Clinically Unsuspected Retinal Neovascularization in Patients with Diabetic Retinopathy.

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] mm2 vs. 0.16 [0.36] mm2; 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.

Deep learning-based signal-independent assessment of macular avascular area on 6×6 mm optical coherence tomography angiogram in diabetic retinopathy: a comparison to instrument-embedded software.

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.

Deep-Learning-Aided Diagnosis of Diabetic Retinopathy, Age-Related Macular Degeneration, and Glaucoma Based on Structural and Angiographic OCT.

Purpose: Timely diagnosis of eye diseases is paramount to obtaining the best treatment outcomes. OCT and OCT angiography (OCTA) have several advantages that lend themselves to early detection of ocular pathology; furthermore, the techniques produce large, feature-rich data volumes. However, the full clinical potential of both OCT and OCTA is stymied when complex data acquired using the techniques must be manually processed. Here, we propose an automated diagnostic framework based on structural OCT and OCTA data volumes that could substantially support the clinical application of these technologies. Design: Cross sectional study. Participants: Five hundred twenty-six OCT and OCTA volumes were scanned from the eyes of 91 healthy participants, 161 patients with diabetic retinopathy (DR), 95 patients with age-related macular degeneration (AMD), and 108 patients with glaucoma. Methods: The diagnosis framework was constructed based on semisequential 3-dimensional (3D) convolutional neural networks. The trained framework classifies combined structural OCT and OCTA scans as normal, DR, AMD, or glaucoma. Fivefold cross-validation was performed, with 60% of the data reserved for training, 20% for validation, and 20% for testing. The training, validation, and test data sets were independent, with no shared patients. For scans diagnosed as DR, AMD, or glaucoma, 3D class activation maps were generated to highlight subregions that were considered important by the framework for automated diagnosis. Main Outcome Measures: The area under the curve (AUC) of the receiver operating characteristic curve and quadratic-weighted kappa were used to quantify the diagnostic performance of the framework. Results: For the diagnosis of DR, the framework achieved an AUC of 0.95 ± 0.01. For the diagnosis of AMD, the framework achieved an AUC of 0.98 ± 0.01. For the diagnosis of glaucoma, the framework achieved an AUC of 0.91 ± 0.02. Conclusions: Deep learning frameworks can provide reliable, sensitive, interpretable, and fully automated diagnosis of eye diseases. Financial Disclosures: Proprietary or commercial disclosure may be found after the references.

A Deep Learning Network for Classifying Arteries and Veins in Montaged Widefield OCT Angiograms.

Purpose: To propose a deep-learning-based method to differentiate arteries from veins in montaged widefield OCT angiography (OCTA). Design: Cross-sectional study. Participants: 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. Methods: 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. Main Outcome Measures: Classification of arteries and veins, arterial and venous caliber, and arterial and venous tortuosity. Results: 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. Conclusions: 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.

Volume-based, layer-independent, disease-agnostic detection of abnormal retinal reflectivity, nonperfusion, and neovascularization using structural and angiographic OCT.

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.

Iatrogenic choroidal neovascularization associated with subretinal gene therapy surgery.

Purpose: To report a case of iatrogenic choroidal neovascularization (CNV) developing one month after subretinal gene therapy surgery. Observations: A 16-year-old male with biallelic RPE65 mutation associated retinal dystrophy was treated with subretinal voretigene neparvovec in the left eye. During initiation of a balanced salt solution pre-bleb, a faint and transient subretinal hemorrhage was observed at the retinotomy site. One month post-operatively, multi-modal imaging detected a CNV and a break in Bruch's membrane at the retinotomy site. The asymptomatic CNV was observed without treatment and resolved spontaneously. Conclusions & importance: As subretinal gene therapy surgery becomes more common, clinicians should monitor for possible trauma induced CNV associated with retinotomy formation and subretinal injection.

THREE-DIMENSIONAL QUANTIFICATION OF INTRARETINAL CYSTOID SPACES ASSOCIATED WITH FULL-THICKNESS MACULAR HOLE.

PURPOSE: To evaluate intraretinal cystoid spaces in patients with idiopathic macular hole (MH). METHODS: 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. RESULTS: Thirty-nine eyes from 39 participants were included. Postoperative 12-month visual acuity correlated with both inner fluid volume and minimum MH size (both P < 0.05) 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). CONCLUSION: Increased inner fluid volume was associated with worse postoperative VA.

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

OBJECTIVE: To detect the plexus-specific retinal capillary avascular area in exudative age-related macular degeneration (EAMD) with projection-resolved optical coherence tomography angiography (PR-OCTA). METHODS AND ANALYSIS: In this prospective cross-sectional single centre study, eyes with treatment-naïve EAMD underwent macular 3×3 mm OCTA with AngioVue system. OCTA scans were analysed and processed including three-dimensional projection artefact removal, retinal layer semi-automated segmentation and en face angiogram generation. Automated quantification of extrafoveal (excluding the central 1 mm circle) avascular area (EAA) were calculated on projection-resolved superficial vascular complex (SVC), intermediate capillary plexus (ICP) and deep capillary plexus (DCP), respectively. RESULTS: Nineteen eyes with EAMD and 19 age-matched healthy control eyes were included. There was no significant difference between the EAMD and control eyes in terms of age, sex, axial length and mean ocular perfusion pressure (all p>0.05). Compared with control eyes, EAMD eyes had significantly larger EAA in SVC (median 0.125 vs 0.059 mm2, p=0.006), ICP (0.016 vs 0.000 mm2, p=0.004) and DCP (0.033 vs 0.000 mm2, p＜0.001). CONCLUSION: PR-OCTA showed that EAMD is associated with focal avascular area in all the three retinal vascular plexuses.