Purpose: To test the feasibility of using deep learning for optical coherence tomography angiography (OCTA) detection of diabetic retinopathy. Methods: A deep-learning convolutional neural network (CNN) architecture, VGG16, was employed for this study. A transfer learning process was implemented to retrain the CNN for robust OCTA classification. One dataset, consisting of images of 32 healthy eyes, 75 eyes with diabetic retinopathy (DR), and 24 eyes with diabetes but no DR (NoDR), was used for training and cross-validation. A second dataset consisting of 20 NoDR and 26 DR eyes was used for external validation. To demonstrate the feasibility of using artificial intelligence (AI) screening of DR in clinical environments, the CNN was incorporated into a graphical user interface (GUI) platform. Results: With the last nine layers retrained, the CNN architecture achieved the best performance for automated OCTA classification. The cross-validation accuracy of the retrained classifier for differentiating among healthy, NoDR, and DR eyes was 87.27%, with 83.76% sensitivity and 90.82% specificity. The AUC metrics for binary classification of healthy, NoDR, and DR eyes were 0.97, 0.98, and 0.97, respectively. The GUI platform enabled easy validation of the method for AI screening of DR in a clinical environment. Conclusions: With a transfer learning process for retraining, a CNN can be used for robust OCTA classification of healthy, NoDR, and DR eyes. The AI-based OCTA classification platform may provide a practical solution to reducing the burden of experienced ophthalmologists with regard to mass screening of DR patients. Translational Relevance: Deep-learning-based OCTA classification can alleviate the need for manual graders and improve DR screening efficiency.

Purpose: This study aims to characterize quantitative optical coherence tomography angiography (OCTA) features of nonproliferative diabetic retinopathy (NPDR) and to validate them for computer-aided NPDR staging. Methods: One hundred and twenty OCTA images from 60 NPDR (mild, moderate, and severe stages) patients and 40 images from 20 control subjects were used for this study conducted in a tertiary, subspecialty, academic practice. Both eyes were photographed and all the OCTAs were 6 mm × 6 mm macular scans. Six quantitative features, that is, blood vessel tortuosity, blood vascular caliber, vessel perimeter index, blood vessel density, foveal avascular zone area, and foveal avascular zone contour irregularity (FAZ-CI) were derived from each OCTA image. A support vector machine classification model was trained and tested for computer-aided classification of NPDR stages. Sensitivity, specificity, and accuracy were used as performance metrics of computer-aided classification, and receiver operation characteristics curve was plotted to measure the sensitivity–specificity tradeoff of the classification algorithm. Results: Among 6 individual OCTA features, blood vessel density shows the best classification accuracies, 93.89% and 90.89% for control versus disease and control versus mild NPDR, respectively. Combined feature classification achieved improved accuracies, 94.41% and 92.96%, respectively. Moreover, the temporal-perifoveal region was the most sensitive region for early detection of DR. For multiclass classification, support vector machine algorithm achieved 84% accuracy. Conclusion: Blood vessel density was observed as the most sensitive feature, and temporal-perifoveal region was the most sensitive region for early detection of DR. Quantitative OCTA analysis enabled computer-aided identification and staging of NPDR.

As a new optical coherence tomography (OCT) modality, OCT angiography (OCTA) provides a noninvasive method to detect microvascular distortions correlated with eye conditions. By providing unparalleled capability to differentiate individual plexus layers in the retina, OCTA has demonstrated its excellence in clinical management of diabetic retinopathy, glaucoma, sickle cell retinopathy, diabetic macular edema, and other eye diseases. Quantitative OCTA analysis of retinal and choroidal vasculatures is essential to standardize objective interpretations of clinical outcome. Quantitative features, including blood vessel tortuosity, blood vessel caliber, blood vessel density, vessel perimeter index, fovea avascular zone area, fovea avascular zone contour irregularity, vessel branching coefficient, vessel branching angle, branching width ratio, and choroidal vascular analysis have been established for objective OCTA assessment. Moreover, differential artery–vein analysis has been recently demonstrated to improve OCTA performance for objective detection and classification of eye diseases. In this review, technical rationales and clinical applications of these quantitative OCTA features are summarized, and future prospects for using these quantitative OCTA features for artificial intelligence classification of eye conditions are discussed.Impact statement: OCT angiography (OCTA) provides a noninvasive method to detect microvascular distortions correlated with eye conditions. Quantitative analysis of OCTA is essential to standardize objective interpretations of clinical outcome. This review summarizes technical rationales and clinical applications of quantitative OCTA features.

Early detection is an essential step for effective intervention of sickle cell retinopathy (SCR). Emerging optical coherence tomography angiography (OCTA) provides excellent three-dimensional (3D) resolution to enable label-free, noninvasive visualization of retinal vascular structures, promising improved sensitivity in detecting SCR. However, quantitative analysis of SCR characteristics in OCTA images is yet to be established. In this study, we conducted comprehensive analysis of six OCTA parameters, including blood vessel tortuosity, vessel diameter, vessel perimeter index (VPI), area of foveal avascular zone (FAZ), contour irregularity of FAZ and parafoveal avascular density. Compared to traditional retinal thickness analysis, five of these six OCTA parameters show improved sensitivity for SCR detection than retinal thickness. It is observed that the most sensitive parameters were the contour irregularity of FAZ in the superficial layer and avascular density in temporal regions, while the area of FAZ, tortuosity and mean diameter of the vessel were moderately sensitive.

Artificial intelligence (AI) classification holds promise as a novel and affordable screening tool for clinical management of ocular diseases. Rural and underserved areas, which suffer from lack of access to experienced ophthalmologists may particularly benefit from this technology. Quantitative optical coherence tomography angiography (OCTA) imaging provides excellent capability to identify subtle vascular distortions, which are useful for classifying retinovascular diseases. However, application of AI for differentiation and classification of multiple eye diseases is not yet established. In this study, we demonstrate supervised machine learning based multi-task OCTA classification. We sought (1) to differentiate normal from diseased ocular conditions, (2) to differentiate different ocular disease conditions from each other, and (3) to stage the severity of each ocular condition. Quantitative OCTA features, including blood vessel tortuosity (BVT), blood vascular caliber (BVC), vessel perimeter index (VPI), blood vessel density (BVD), foveal avascular zone (FAZ) area (FAZ-A), and FAZ contour irregularity (FAZ-CI) were fully automatically extracted from the OCTA images. A stepwise backward elimination approach was employed to identify sensitive OCTA features and optimal-feature-combinations for the multi-task classification. For proof-of-concept demonstration, diabetic retinopathy (DR) and sickle cell retinopathy (SCR) were used to validate the supervised machine leaning classifier. The presented AI classification methodology is applicable and can be readily extended to other ocular diseases, holding promise to enable a mass-screening platform for clinical deployment and telemedicine.

This study is to demonstrate deep learning for automated artery-vein (AV) classification in optical coherence tomography angiography (OCTA). The AV-Net, a fully convolutional network (FCN) based on modified U-shaped CNN architecture, incorporates enface OCT and OCTA to differentiate arteries and veins. For the multi-modal training process, the enface OCT works as a near infrared fundus image to provide vessel intensity profiles, and the OCTA contains blood flow strength and vessel geometry features. A transfer learning process is also integrated to compensate for the limitation of available dataset size of OCTA, which is a relatively new imaging modality. By providing an average accuracy of 86.75%, the AV-Net promises a fully automated platform to foster clinical deployment of differential AV analysis in OCTA.