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.

As a new optical coherence tomography (OCT) imaging modality, there is no standardized quantitative interpretation of OCT angiography (OCTA) characteristics of sickle cell retinopathy (SCR). This study is to demonstrate computer-aided SCR classification using quantitative OCTA features, i.e., blood vessel tortuosity (BVT), blood vessel diameter (BVD), vessel perimeter index (VPI), foveal avascular zone (FAZ) area, FAZ contour irregularity, parafoveal avascular density (PAD). It was observed that combined features show improved classification performance, compared to single feature. Three classifiers, including support vector machine (SVM), k-nearest neighbor (KNN) algorithm, and discriminant analysis, were evaluated. Sensitivity, specificity, and accuracy were quantified to assess the performance of each classifier. For SCR vs. control classification, all three classifiers performed well with an average accuracy of 95% using the six quantitative OCTA features. For mild vs. severe stage retinopathy classification, SVM shows better (97% accuracy) performance, compared to KNN algorithm (95% accuracy) and discriminant analysis (88% accuracy).

To discuss the worldwide applications and potential impact of artificial intelligence (AI) for the diagnosis, management and analysis of treatment outcomes of common retinal diseases. We performed an online literature review, using PubMed Central (PMC), of AI applications to evaluate and manage retinal diseases. Search terms included AI for screening, diagnosis, monitoring, management, and treatment outcomes for age-related macular degeneration (AMD), diabetic retinopathy (DR), retinal surgery, retinal vascular disease, retinopathy of prematurity (ROP) and sickle cell retinopathy (SCR). Additional search terms included AI and color fundus photographs, optical coherence tomography (OCT), and OCT angiography (OCTA). We included original research articles and review articles. Research studies have investigated and shown the utility of AI for screening for diseases such as DR, AMD, ROP, and SCR. Research studies using validated and labeled datasets confirmed AI algorithms could predict disease progression and response to treatment. Studies showed AI facilitated rapid and quantitative interpretation of retinal biomarkers seen on OCT and OCTA imaging. Research articles suggest AI may be useful for planning and performing robotic surgery. Studies suggest AI holds the potential to help lessen the impact of socioeconomic disparities on the outcomes of retinal diseases. AI applications for retinal diseases can assist the clinician, not only by disease screening and monitoring for disease recurrence but also in quantitative analysis of treatment outcomes and prediction of treatment response. The public health impact on the prevention of blindness from DR, AMD, and other retinal vascular diseases remains to be determined.