Effect of manual OCTA segmentation correction to improve image quality and visibility of choroidal neovascularization in AMD.

In this retrospective case series on neovascular age-related macular degeneration (nAMD), we aimed to improve Choroidal Neovascularization (CNV) visualization in Optical Coherence Tomography Angiography (OCTA) scans by addressing segmentation errors. Out of 198 eyes, 73 OCTA scans required manual segmentation correction. We compared uncorrected scans to those with minimal (2 corrections), moderate (10 corrections), and detailed (50 corrections) efforts targeting falsely segmented Bruch's Membrane (BM). Results showed that 55% of corrected OCTAs exhibited improved quality after manual correction. Notably, minimal correction (2 scans) already led to significant improvements, with additional corrections (10 or 50) not further enhancing expert grading. Reduced background noise and improved CNV identification were observed, with the most substantial improvement after two corrections compared to baseline uncorrected images. In conclusion, our approach of correcting segmentation errors effectively enhances image quality in OCTA scans of nAMD. This study demonstrates the efficacy of the method, with 55% of resegmented OCTA images exhibiting enhanced quality, leading to a notable increase in the proportion of high-quality images from 63 to 83%.

ARTIFICIAL INTELLIGENCE FOR OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY-BASED DISEASE ACTIVITY PREDICTION IN AGE-RELATED MACULAR DEGENERATION.

PURPOSE: The authors hypothesize that optical coherence tomography angiography (OCTA)-visualized vascular morphology may be a predictor of choroidal neovascularization status in age-related macular degeneration (AMD). The authors thus evaluated the use of artificial intelligence (AI) to predict different stages of AMD disease based on OCTA en face 2D projections scans. METHODS: Retrospective cross-sectional study based on collected 2D OCTA data from 310 high-resolution scans. Based on OCT B-scan fluid and clinical status, OCTA was classified as normal, dry AMD, wet AMD active, and wet AMD in remission with no signs of activity. Two human experts graded the same test set, and a consensus grading between two experts was used for the prediction of four categories. RESULTS: The AI can achieve 80.36% accuracy on a four-category grading task with 2D OCTA projections. The sensitivity of prediction by AI was 0.7857 (active), 0.7142 (remission), 0.9286 (dry AMD), and 0.9286 (normal) and the specificity was 0.9524, 0.9524, 0.9286, and 0.9524, respectively. The sensitivity of prediction by human experts was 0.4286 active choroidal neovascularization, 0.2143 remission, 0.8571 dry AMD, and 0.8571 normal with specificity of 0.7619, 0.9286, 0.7857, and 0.9762, respectively. The overall AI classification prediction was significantly better than the human (odds ratio = 1.95, P = 0.0021). CONCLUSION: These data show that choroidal neovascularization morphology can be used to predict disease activity by AI; longitudinal studies are needed to better understand the evolution of choroidal neovascularization and features that predict reactivation. Future studies will be able to evaluate the additional predicative value of OCTA on top of other imaging characteristics (i.e., fluid location on OCT B scans) to help predict response to treatment.

Ultra-wide field and new wide field composite retinal image registration with AI-enabled pipeline and 3D distortion correction algorithm.

PURPOSE: This study aimed to compare a new Artificial Intelligence (AI) method to conventional mathematical warping in accurately overlaying peripheral retinal vessels from two different imaging devices: confocal scanning laser ophthalmoscope (cSLO) wide-field images and SLO ultra-wide field images. METHODS: Images were captured using the Heidelberg Spectralis 55-degree field-of-view and Optos ultra-wide field. The conventional mathematical warping was performed using Random Sample Consensus-Sample and Consensus sets (RANSAC-SC). This was compared to an AI alignment algorithm based on a one-way forward registration procedure consisting of full Convolutional Neural Networks (CNNs) with Outlier Rejection (OR CNN), as well as an iterative 3D camera pose optimization process (OR CNN + Distortion Correction [DC]). Images were provided in a checkerboard pattern, and peripheral vessels were graded in four quadrants based on alignment to the adjacent box. RESULTS: A total of 660 boxes were analysed from 55 eyes. Dice scores were compared between the three methods (RANSAC-SC/OR CNN/OR CNN + DC): 0.3341/0.4665/4784 for fold 1-2 and 0.3315/0.4494/4596 for fold 2-1 in composite images. The images composed using the OR CNN + DC have a median rating of 4 (out of 5) versus 2 using RANSAC-SC. The odds of getting a higher grading level are 4.8 times higher using our OR CNN + DC than RANSAC-SC (p < 0.0001). CONCLUSION: Peripheral retinal vessel alignment performed better using our AI algorithm than RANSAC-SC. This may help improve co-localizing retinal anatomy and pathology with our algorithm.

A Novel Mouse Model for Late-Onset Retinal Degeneration (L-ORD) Develops RPE Abnormalities Due to the Loss of C1qtnf5/Ctrp5.

Late-onset retinal degeneration (L-ORD) is an autosomal dominant macular dystrophy resulting from mutations in the gene CTRP5/C1QTNF5. A mouse model (Ctrp5+/-) for the most common S163R developed many features of human clinical disease. We generated a novel homozygous Ctrp5 gene knock-out (Ctrp5-/-) mouse model to further study the mechanism of L-ORD. The retinal morphology of these mice was evaluated by retinal imaging, light microscopy, and transmission electron microscopy (TEM) at 6, 11, and 18.5 mo. Expression of Ctrp5 was analyzed using immunostaining and qRT-PCR. The Ctrp5-/- mice showed lack of both Ctrp5 transcript and protein. Presence of a significantly larger number of autofluorescent spots was observed in Ctrp5-/- mice compared to the WT (P < 0.0001) at 19 mo. Increased RPE stress with vacuolization and thinning was observed as early as 6 mo in Ctrp5-/- mice. Further, ultrastructural analyses revealed a progressive accumulation of basal laminar sub-RPE deposits in Ctrp5-/- mice from 11 mo. The Ctrp5-/- mice shared retinal and RPE pathology that matches with that previously described for Ctrp5+/- mice suggesting that pathology in these mice results from the loss of functional CTRP5 and that the presence of CTRP5 is critical for normal RPE and retinal function.

Inner Retinal Thickness and Vasculature in Patients with Reticular Pseudodrusen.

INTRODUCTION: The aim of this study was to investigate retinal layer thickness and vessel density differences between patients with reticular pseudodrusen (RPD) and intermediate dry age-related macular degeneration (iAMD). METHODS: Participants included in the study were patients diagnosed by retinal specialists with RPD, iAMD, and both RPD and iAMD at our academic referral center, seen from May 2021 until February 2022. The central 3 mm retinal thickness was measured using spectral-domain optical coherence tomography (Heidelberg Spectralis HRA+OCT System; Heidelberg Engineering, Heidelberg, Germany). Individual retinal thickness measurements were obtained from the innermost layer (nerve fiber layer) until the outermost layer (retinal pigment epithelium [RPE]). Each thickness measurement was subdivided into nine Early Treatment Diabetic Retinopathy Study (ETDRS) sectors. For the vessel density, OCT angiography from the Heidelberg Spectralis System was measured using proprietary third-party software (AngioTool; National Institutes of Health, National Cancer Institute, Bethesda, MD). Clinical and demographic characteristics were compared across the three groups (iAMD, RPD, iAMD and RPD) and analyzed with necessary adjustments. Linear mixed-effects models with necessary corrections were employed to compare continuous eye-level measurements between our three groups as well as in pairwise fashion using the R statistical programming software (R version 4.2.1). RESULTS: A total of 25 eyes of 17 patients with RPD, 20 eyes of 15 patients with iAMD, and 14 eyes of 9 patients with both iAMD and RPD were analyzed. Retinal thickness analysis identified that the superior inner (p = 0.028) and superior outer (p = 0.027) maculas of eyes with both iAMD and RPD were significantly thinner than those with iAMD alone. In eyes with RPD, the superior inner and superior outer RPE (p = 0.011 and p = 0.05, respectively), outer plexiform layer (p = 0.003 and p = 0.013, respectively), and inner nuclear layer (p = 0.034 and p = 0, respectively) were noted to be thinner compared to eyes with iAMD alone. In addition, the macular deep capillary plexus vessel density was significantly reduced in eyes with RPD compared to eyes with iAMD (p = 0.017). CONCLUSION: Patients with RPD had inner retinal structural as well as vascular changes compared to iAMD patients. Inner retinal vascular attenuation should be investigated further to see if there is a causal association with retinal thinning.

Efficacy and accuracy of artificial intelligence to overlay multimodal images from different optical instruments in patients with retinitis pigmentosa.

BACKGROUND: Retinitis pigmentosa (RP) represents a group of progressive, genetically heterogenous blinding diseases. Recently, relationships between measures of retinal function and structure are needed to help identify outcome measures or biomarkers for clinical trials. The ability to align retinal multimodal images, taken on different platforms, will allow better understanding of this relationship. We investigate the efficacy of artificial intelligence (AI) in overlaying different multimodal retinal images in RP patients. METHODS: We overlayed infrared images from microperimetry on near-infra-red images from scanning laser ophthalmoscope and spectral domain optical coherence tomography in RP patients using manual alignment and AI. The AI adopted a two-step framework and was trained on a separate dataset. Manual alignment was performed using in-house software that allowed labelling of six key points located at vessel bifurcations. Manual overlay was considered successful if the distance between same key points on the overlayed images was ≤1/2°. RESULTS: Fifty-seven eyes of 32 patients were included in the analysis. AI was significantly more accurate and successful in aligning images compared to manual alignment as confirmed by linear mixed-effects modelling (p < 0.001). A receiver operating characteristic analysis, used to compute the area under the curve of the AI (0.991) and manual (0.835) Dice coefficients in relation to their respective 'truth' values, found AI significantly more accurate in the overlay (p < 0.001). CONCLUSION: AI was significantly more accurate than manual alignment in overlaying multimodal retinal imaging in RP patients and showed the potential to use AI algorithms for future multimodal clinical and research applications.

Retinal tissue and microvasculature loss in COVID-19 infection.

This cross-sectional study aimed to investigate the hypothesis that permanent capillary damage may underlie the long-term COVID-19 sequela by quantifying the retinal vessel integrity. Participants were divided into three subgroups; Normal controls who had not been affected by COVID-19, mild COVID-19 cases who received out-patient care, and severe COVID-19 cases requiring intensive care unit (ICU) admission and respiratory support. Patients with systemic conditions that may affect the retinal vasculature before the diagnosis of COVID-19 infection were excluded. Participants underwent comprehensive ophthalmologic examination and retinal imaging obtained from Spectral-Domain Optical Coherence Tomography (SD-OCT), and vessel density using OCT Angiography. Sixty-one eyes from 31 individuals were studied. Retinal volume was significantly decreased in the outer 3 mm of the macula in the severe COVID-19 group (p = 0.02). Total retinal vessel density was significantly lower in the severe COVID-19 group compared to the normal and mild COVID-19 groups (p = 0.004 and 0.0057, respectively). The intermediate and deep capillary plexuses in the severe COVID-19 group were significantly lower compared to other groups (p < 0.05). Retinal tissue and microvascular loss may be a biomarker of COVID-19 severity. Further monitoring of the retina in COVID-19-recovered patients may help further understand the COVID-19 sequela.

Accuracy and Time Comparison Between Side-by-Side and Artificial Intelligence Overlayed Images.

BACKGROUND AND OBJECTIVE: The purpose of this study was to evaluate the accuracy and the time to find a lesion, taken in different platforms, color fundus photographs and infrared scanning laser ophthalmoscope images, using the traditional side-by-side (SBS) colocalization technique to an artificial intelligence (AI)-assisted technique. PATIENTS AND METHODS: Fifty-three pathological lesions were studied in 11 eyes. Images were aligned using SBS and AI overlaid methods. The location of each color fundus lesion on the corresponding infrared scanning laser ophthalmoscope image was analyzed twice, one time for each method, on different days, for two specialists, in random order. The outcomes for each method were measured and recorded by an independent observer. RESULTS: The colocalization AI method was superior to the conventional in accuracy and time (P < .001), with a mean time to colocalize 37% faster. The error rate using AI was 0% compared with 18% in SBS measurements. CONCLUSIONS: AI permitted a more accurate and faster colocalization of pathologic lesions than the conventional method. [Ophthalmic Surg Lasers Imaging Retina 2023;54:108-113.].

Automated Detection of Posterior Vitreous Detachment on OCT Using Computer Vision and Deep Learning Algorithms.

Objective: To develop automated algorithms for the detection of posterior vitreous detachment (PVD) using OCT imaging. Design: Evaluation of a diagnostic test or technology. Subjects: Overall, 42 385 consecutive OCT images (865 volumetric OCT scans) obtained with Heidelberg Spectralis from 865 eyes from 464 patients at an academic retina clinic between October 2020 and December 2021 were retrospectively reviewed. Methods: We developed a customized computer vision algorithm based on image filtering and edge detection to detect the posterior vitreous cortex for the determination of PVD status. A second deep learning (DL) image classification model based on convolutional neural networks and ResNet-50 architecture was also trained to identify PVD status from OCT images. The training dataset consisted of 674 OCT volume scans (33 026 OCT images), while the validation testing set consisted of 73 OCT volume scans (3577 OCT images). Overall, 118 OCT volume scans (5782 OCT images) were used as a separate external testing dataset. Main Outcome Measures: Accuracy, sensitivity, specificity, F1-scores, and area under the receiver operator characteristic curves (AUROCs) were measured to assess the performance of the automated algorithms. Results: Both the customized computer vision algorithm and DL model results were largely in agreement with the PVD status labeled by trained graders. The DL approach achieved an accuracy of 90.7% and an F1-score of 0.932 with a sensitivity of 100% and a specificity of 74.5% for PVD detection from an OCT volume scan. The AUROC was 89% at the image level and 96% at the volume level for the DL model. The customized computer vision algorithm attained an accuracy of 89.5% and an F1-score of 0.912 with a sensitivity of 91.9% and a specificity of 86.1% on the same task. Conclusions: Both the computer vision algorithm and the DL model applied on OCT imaging enabled reliable detection of PVD status, demonstrating the potential for OCT-based automated PVD status classification to assist with vitreoretinal surgical planning. Financial Disclosures: Proprietary or commercial disclosure may be found after the references.

ACCURATE REGISTRATION BETWEEN ULTRA-WIDE-FIELD AND NARROW ANGLE RETINA IMAGES WITH 3D EYEBALL SHAPE OPTIMIZATION.

The Ultra-Wide-Field (UWF) retina images have attracted wide attentions in recent years in the study of retina. However, accurate registration between the UWF images and the other types of retina images could be challenging due to the distortion in the peripheral areas of an UWF image, which a 2D warping can not handle. In this paper, we propose a novel 3D distortion correction method which sets up a 3D projection model and optimizes a dense 3D retina mesh to correct the distortion in the UWF image. The corrected UWF image can then be accurately aligned to the target image using 2D alignment methods. The experimental results show that our proposed method outperforms the state-of-the-art method by 30%.

A Mouse Model with Ablated Asparaginase and Isoaspartyl Peptidase 1 (Asrgl1) Develops Early Onset Retinal Degeneration (RD) Recapitulating the Human Phenotype.

We previously identified a homozygous G178R mutation in human ASRGL1 (hASRGL1) through whole-exome analysis responsible for early onset retinal degeneration (RD) in patients with cone-rod dystrophy. The mutant G178R ASRGL1 expressed in Cos-7 cells showed altered localization, while the mutant ASRGL1 in E. coli lacked the autocatalytic activity needed to generate the active protein. To evaluate the effect of impaired ASRGL1 function on the retina in vivo, we generated a mouse model with c.578_579insAGAAA (NM_001083926.2) mutation (Asrgl1mut/mut) through the CRISPR/Cas9 methodology. The expression of ASGRL1 and its asparaginase activity were undetectable in the retina of Asrgl1mut/mut mice. The ophthalmic evaluation of Asrgl1mut/mut mice showed a significant and progressive decrease in scotopic electroretinographic (ERG) response observed at an early age of 3 months followed by a decrease in photopic response around 5 months compared with age-matched wildtype mice. Immunostaining and RT-PCR analyses with rod and cone cell markers revealed a loss of cone outer segments and a significant decrease in the expression of Rhodopsin, Opn1sw, and Opn1mw at 3 months in Asrgl1mut/mut mice compared with age-matched wildtype mice. Importantly, the retinal phenotype of Asrgl1mut/mut mice is consistent with the phenotype observed in patients harboring the G178R mutation in ASRGL1 confirming a critical role of ASRGL1 in the retina and the contribution of ASRGL1 mutations in retinal degeneration.

Two-Step Registration on Multi-Modal Retinal Images via Deep Neural Networks.

Multi-modal retinal image registration plays an important role in the ophthalmological diagnosis process. The conventional methods lack robustness in aligning multi-modal images of various imaging qualities. Deep-learning methods have not been widely developed for this task, especially for the coarse-to-fine registration pipeline. To handle this task, we propose a two-step method based on deep convolutional networks, including a coarse alignment step and a fine alignment step. In the coarse alignment step, a global registration matrix is estimated by three sequentially connected networks for vessel segmentation, feature detection and description, and outlier rejection, respectively. In the fine alignment step, a deformable registration network is set up to find pixel-wise correspondence between a target image and a coarsely aligned image from the previous step to further improve the alignment accuracy. Particularly, an unsupervised learning framework is proposed to handle the difficulties of inconsistent modalities and lack of labeled training data for the fine alignment step. The proposed framework first changes multi-modal images into a same modality through modality transformers, and then adopts photometric consistency loss and smoothness loss to train the deformable registration network. The experimental results show that the proposed method achieves state-of-the-art results in Dice metrics and is more robust in challenging cases.

Perspective Distortion Correction for Multi-Modal Registration between Ultra-Widefield and Narrow-Angle Retinal Images.

Multi-modal retinal image registration between 2D Ultra-Widefield (UWF) and narrow-angle (NA) images has not been well-studied, since most existing methods mainly focus on NA image alignment. The stereographic projection model used in UWF imaging causes strong distortions in peripheral areas, which leads to inferior alignment quality. We propose a distortion correction method that remaps the UWF images based on estimated camera view points of NA images. In addition, we set up a CNN-based registration pipeline for UWF and NA images, which consists of the distortion correction method and three networks for vessel segmentation, feature detection and matching, and outlier rejection. Experimental results on our collected dataset shows the effectiveness of the proposed pipeline and the distortion correction method.

Assessing the validity of a cross-platform retinal image segmentation tool in normal and diseased retina.

Comparing automated retinal layer segmentation using proprietary software (Heidelberg Spectralis HRA + OCT) and cross-platform Optical Coherence Tomography (OCT) segmentation software (Orion). Image segmentations of normal and diseased (iAMD, DME) eyes were performed using both softwares and then compared to the 'gold standard' of manual segmentation. A qualitative assessment and quantitative (layer volume) comparison of segmentations were performed. Segmented images from the two softwares were graded by two masked graders and in cases with difference, a senior retina specialist made a final independent decisive grading. Cross-platform software was significantly better than the proprietary software in the segmentation of NFL and INL layers in Normal eyes. It generated significantly better segmentation only for NFL in iAMD and for INL and OPL layers in DME eyes. In normal eyes, all retinal layer volumes calculated by the two softwares were moderate-strongly correlated except OUTLY. In iAMD eyes, GCIPL, INL, ONL, INLY, TRV layer volumes were moderate-strongly correlated between softwares. In eyes with DME, all layer volume values were moderate-strongly correlated between softwares. Cross-platform software can be used reliably in research settings to study the retinal layers as it compares well against manual segmentation and the commonly used proprietary software for both normal and diseased eyes.

CHOROIDAL IMAGING BIOMARKERS TO PREDICT HIGHLY RESPONSIVE AND RESISTANT CASES TREATED WITH STANDARDIZED ANTI-VASCULAR ENDOTHELIAL GROWTH FACTOR REGIMEN IN NEOVASCULAR AGE-RELATED MACULAR DEGENERATION.

PURPOSE: To determine structural predictors of treatment response in neovascular age-related macular degeneration analyzing optical coherence tomography (OCT)-related biomarkers. METHODS: A retrospective review of patients undergoing treatment for neovascular age-related macular degeneration at a tertiary institute was performed at presentation. High-intensity regimen included eyes on long-term anti-vascular endothelial growth factor treatment with the inability to extend beyond a month without a relapse and needed double the dose of medication (n = 25). Low-intensity regimen had eyes that went into long-term remission after at least three injections and remained dry for more than a year until the last visit (n = 20). Multimodal imaging including fluorescein angiogram, OCT, and comprehensive ocular evaluation were done. Choroidal vascularity index, total choroidal area, luminal area, subfoveal choroidal thickness, choriocapillaris thickness and Haller and Sattler layer thickness were analyzed for statistical significance. RESULTS: The groups had no significant difference at baseline in age, gender, incidence of reticular pseudodrusen, polypoidal choroidal vasculopathy feature on OCT, type of choroidal neovascular membrane, and geographic atrophy. Multinomial logistic regression revealed that thicker subfoveal choroidal thickness and larger total choroidal area were the significant predictors of poor response to anti-vascular endothelial growth factor treatment (E = 0.02; P = 0.02; E = 1.82; P = 0.0075). CONCLUSION: Thicker subfoveal choroidal thickness and higher total choroidal area are useful variables to predict a poor treatment response.

Robust Content-Adaptive Global Registration for Multimodal Retinal Images Using Weakly Supervised Deep-Learning Framework.

Multimodal retinal imaging plays an important role in ophthalmology. We propose a content-adaptive multimodal retinal image registration method in this paper that focuses on the globally coarse alignment and includes three weakly supervised neural networks for vessel segmentation, feature detection and description, and outlier rejection. We apply the proposed framework to register color fundus images with infrared reflectance and fluorescein angiography images, and compare it with several conventional and deep learning methods. Our proposed framework demonstrates a significant improvement in robustness and accuracy reflected by a higher success rate and Dice coefficient compared with other methods.

LEARNING TO CORRECT AXIAL MOTION IN OCT FOR 3D RETINAL IMAGING.

Optical Coherence Tomography (OCT) is a powerful technique for non-invasive 3D imaging of biological tissues at high resolution that has revolutionized retinal imaging. A major challenge in OCT imaging is the motion artifacts introduced by involuntary eye movements. In this paper, we propose a convolutional neural network that learns to correct axial motion in OCT based on a single volumetric scan. The proposed method is able to correct large motion, while preserving the overall curvature of the retina. The experimental results show significant improvements in visual quality as well as overall error compared to the conventional methods in both normal and disease cases.

Influence of vitrectomy on the progression of dry age-related macular degeneration.

PURPOSE: To demonstrate whether pars plana vitrectomy (PPV) changes the progression of dry age-related macular degeneration (AMD) by assessing longitudinal changes in drusen volume over follow-up. METHODS: Dry AMD patients who had undergone unilateral PPV for symptomatic vitreomacular disorders were evaluated for the progression of disease by spectral domain-optical coherence tomography (SD-OCT) features including drusen volume, development of geographic atrophy, or choroidal neovascularization during follow-up. Drusen volume was manually calculated using an image processing software (ImageJ, NIH) on raster SD-OCT scans. Mean change in drusen volume of surgery eyes was compared with values of the fellow eyes of the same subjects (control group). RESULTS: Among 183 eyes with both vitreoretinal disorder and dry AMD, 48 eyes of 24 patients met the inclusion criteria and were included. The mean drusen volume change during a mean of 25.49 ± 23.35 months of follow-up (range: 6.00-86.87 months) was 4.236.899 ± 20.488.913 µm3 in the study eye and 7.796.357 ± 34.798.519 µm3 in the fellow eye (p = 0.297). Best-corrected visual acuity (BCVA) significantly increased from 0.40 ± 0.18 logMAR (≈ 20/50 Snellen equivalent) to 0.32 ± 0.31 (≈ 20/41 Snellen equivalent) after surgery (p = 0.012) in the study group while BCVA remained stable in the control group (0.19 ± 0.34 logMAR [≈ 20/30 Snellen equivalent] at baseline and 0.20 ± 0.31 logMAR [≈ 20/31 Snellen equivalent], p = 0.432). Choroidal neovascularization developed in 1 vitrectomized eye (4.54%) and in 1 eye (4.54%) from the control group during follow-up. CONCLUSION: Vitrectomy did not seem to worsen dry AMD progression; even more visual acuity may improve despite a slight increase in drusen volume following surgery.

Evaluation of the clinical utility of optical coherence tomography angiography in age-related macular degeneration.

BACKGROUND/AIMS: To evaluate the ability of optical coherence tomography angiography (OCTA) to identify the presence or absence of choroidal neovascularisation (CNV) and CNV activity in age-related macular degeneration (AMD). METHODS: Clinical parameters, fundus fluorescein angiogram and spectral-domain optical coherence tomography (SD-OCT) were used as the gold standard to determine disease activity. OCTA imaging was performed on the same day and was graded by two masked retina specialists for the presence or absence of CNV. Traditional multimodal imaging and OCTA findings were compared. RESULTS: One hundred and fifty-two eyes of 106 patients with AMD were retrospectively reviewed. Of these, 59 eyes had wet AMD and 93 had dry AMD with high-risk drusen. OCTA had 85.4% and 79.3% specificity and sensitivity, respectively, in determining the presence or absence of CNV. OCTA was 69.5% accurate in determining active CNV. False positives and negatives were 21.6% and 8.0%, respectively. CONCLUSIONS: This study suggests that en-face OCTA images allow a moderate ability to identify CNV and that OCTA alone is weak at recognising active CNV requiring treatment in AMD.