IMPORTANCEThe repeatability and reproducibility of quantitative metrics from optical coherence tomographic angiography (OCTA) must be assessed before these data can be confidently interpreted in clinical research and practice.OBJECTIVE To evaluate the repeatability and reproducibility of OCTA-derived retinal vascular quantitative metrics. DESIGN, SETTING AND PARTICIPANTSIn this cross-sectional study, 21 healthy volunteers (42 eyes) and 22 patients with retinal disease (22 eyes), including 14 with age-related macular degeneration, 3 with epiretinal membrane, 2 with diabetic retinopathy, 2 with myopic macular degeneration, and 1 with retinal vein occlusion, were enrolled. Participants were recruited from September 1 through November 31, 2016. Each eye underwent 3 repeated scans with 3 instruments for a total of 9 acquisitions. Eyes were randomly assigned to scanning with a 3 × 3-mm or 6 × 6-mm pattern. Eyes were excluded from subsequent analysis if any acquisition had a signal strength of less than 7. Repeatability (defined as the agreement in measurements within a device) and reproducibility (defined as the agreement between devices of the same type) were assessed by intraclass correlation coefficient (ICC) and coefficient of variation.EXPOSURES All eyes underwent scanning using 3 separate devices. MAIN OUTCOMES AND MEASURES Vessel length density (VLD) and perfusion density (PD) of the superficial retinal vasculature.RESULTS A total of 21 healthy volunteers (8 men and 13 women; mean [SD] age, 36 [6] years) and 22 patients with retinal disease (15 men and 7 women; mean [SD] age, 79 [9] years) underwent evaluation. Of these, 40 of 42 normal eyes and 15 of 22 eyes with retinal disease met signal strength criteria and were included in this analysis. The ICC among the 3 consecutive scans ranged from 0.82 to 0.98 for VLD and from 0.83 to 0.95 for PD. The coefficient of variation (CV) ranged from 2.2% to 5.9% for VLD and from 2.4% to 5.9% for PD. For reproducibility, the ICC ranged from 0.62 to 0.95 and the CV was less than 6% in all groups. The agreement was highest for the 3 × 3-mm pattern in the inner ring (ICC range, 0.92 [95% CI, 0.85-0.96] to 0.96 [95% CI, 0.93-0.98]) and 6 × 6-mm pattern in the outer ring (ICC range, 0.93 [95% CI, 0.86-0.97] to 0.96 [95% CI, 0.92-0.98]).CONCLUSIONS AND RELEVANCE Vessel length density and PD of the superficial retinal vasculature can be obtained from OCTA images with high levels of repeatability and reproducibility but can vary with scan pattern and location.

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Imaging of the choriocapillaris in vivo is challenging with existing technology. Optical coherence tomography angiography (OCTA), if optimized, could make the imaging less challenging.OBJECTIVE To investigate multiple en face image averaging on OCTA images of the choriocapillaris.DESIGN, SETTING, AND PARTICIPANTS Observational, cross-sectional case series at a referral institutional practice in Los Angeles, California. From the original cohort of 21 healthy individuals, 17 normal eyes of 17 participants were included in the study. The study dates were August to September 2016.EXPOSURES All participants underwent OCTA imaging of the macula covering a 3 × 3-mm area using OCTA software (Cirrus 5000 with AngioPlex; Carl Zeiss Meditec). One eye per participant was repeatedly imaged to obtain 9 OCTA cube scan sets. Registration was first performed using superficial capillary plexus images, and this transformation was then applied to the choriocapillaris images. The 9 registered choriocapillaris images were then averaged. Quantitative parameters were measured on binarized OCTA images and compared with the unaveraged OCTA images. MAIN OUTCOME AND MEASURE Vessel caliber measurement.RESULTS Seventeen eyes of 17 participants (mean [SD] age, 35.1 [6.0] years; 9 [53%] female; and 9 [53%] of white race/ethnicity) with sufficient image quality were included in this analysis. The single unaveraged images demonstrated a granular appearance, and the vascular pattern was difficult to discern. After averaging, en face choriocapillaris images showed a meshwork appearance. The mean (SD) diameter of the vessels was 22.8 (5.8) μm (range, 9.6-40.2 μm). Compared with the single unaveraged images, the averaged images showed more flow voids (1423( flow voids [95% CI, 967-1909 vs 1254 flow voids [95% CI, 825-1683], P < .001), smaller average size of the flow voids (911 [95% CI, 301-1521] μm 2 vs 1364 [95% CI, 645-2083] μm 2 , P < .001), and greater vessel density (70.7% [95% CI, 61.9%-79.5%] vs 61.9% [95% CI, 56.0%-67.8%], P < .001). The distribution of the number vs sizes of the flow voids was skewed in both unaveraged and averaged images. A linear log-log plot of the distribution showed a more homogeneous distribution in the averaged images compared with the unaveraged images.CONCLUSIONS AND RELEVANCE Multiple en face averaging can improve visualization of the choriocapillaris on OCTA images, transforming the images from a granular appearance to a level where the intervascular spaces can be resolved in healthy volunteers.

A simple scoring system relevant to prognosis for early AMD, and practical for use in a busy clinic, can be developed using SD-OCT criteria alone.

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Background Diabetic retinopathy screening is instrumental to preventing blindness, but scaling up screening is challenging because of the increasing number of patients with all forms of diabetes. We aimed to create a deep-learning system to predict the risk of patients with diabetes developing diabetic retinopathy within 2 years. MethodsWe created and validated two versions of a deep-learning system to predict the development of diabetic retinopathy in patients with diabetes who had had teleretinal diabetic retinopathy screening in a primary care setting. The input for the two versions was either a set of three-field or one-field colour fundus photographs. Of the 575 431 eyes in the development set 28 899 had known outcomes, with the remaining 546 532 eyes used to augment the training process via multitask learning. Validation was done on one eye (selected at random) per patient from two datasets: an internal validation (from EyePACS, a teleretinal screening service in the USA) set of 3678 eyes with known outcomes and an external validation (from Thailand) set of 2345 eyes with known outcomes. Findings The three-field deep-learning system had an area under the receiver operating characteristic curve (AUC) of 0•79 (95% CI 0•77-0•81) in the internal validation set. Assessment of the external validation set-which contained only one-field colour fundus photographs-with the one-field deep-learning system gave an AUC of 0•70 (0•67-0•74). In the internal validation set, the AUC of available risk factors was 0•72 (0•68-0•76), which improved to 0•81 (0•77-0•84) after combining the deep-learning system with these risk factors (p<0•0001). In the external validation set, the corresponding AUC improved from 0•62 (0•58-0•66) to 0•71 (0•68-0•75; p<0•0001) following the addition of the deep-learning system to available risk factors.Interpretation The deep-learning systems predicted diabetic retinopathy development using colour fundus photographs, and the systems were independent of and more informative than available risk factors. Such a risk stratification tool might help to optimise screening intervals to reduce costs while improving vision-related outcomes.Funding Google.

We report data for pediatric OCTA parameters in healthy subjects. Several variables influence the density of macular microvascular networks, and these factors should be considered in the OCTA study of pediatric eye disorders.

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