Deep learning in retinal image analysis achieves excellent accuracy for the differential detection of retinal fluid types across the most prevalent exudative macular diseases and OCT devices. Furthermore, quantification of fluid achieves a high level of concordance with manual expert assessment. Fully automated analysis of retinal OCT images from clinical routine provides a promising horizon in improving accuracy and reliability of retinal diagnosis for research and clinical practice in ophthalmology.

IMPORTANCE Robust and sensitive imaging biomarkers for visual function are an unmet medical need in the management of neovascular age-related macular degeneration. OBJECTIVE To determine the correlation of 3-dimensionally quantified intraretinal cystoid fluid (IRC) and subretinal fluid (SRF) with best-corrected visual acuity (BCVA) in treatment-naive neovascular age-related macular degeneration and during antiangiogenic therapy. DESIGN, SETTING, AND PARTICIPANTS Retrospective cohort study between November 2009 and November 2011 at an institutional referral center and reading center of patients with treatment-naive subfoveal choroidal neovascularization receiving intravitreal ranibizumab or aflibercept over 12 months. All individual IRC and SRF lesions were manually delineated on each of the 128 B-scan sections of spectral-domain optical coherence tomographic volume scans at baseline and months 1, 6, and 12. Correlations were computed between the IRC and SRF parameters and the baseline BCVA, final BCVA, and BCVA change. A systematic parameter search was conducted to detect annotation-derived variables with best predictive value. An exponential model for BCVA change balancing for the ceiling effect was constructed. MAIN OUTCOMES AND MEASURES Goodness of fit of correlations between the IRC and SRF parameters and the baseline BCVA, final BCVA, and BCVA change. RESULTS Thirty-eight patients were included (25 female, 13 male; mean [SD] age at enrollment, 78.49 [8.23] years; mean [SD] BCVA score at baseline, 54 [16] Early Treatment Diabetic Retinopathy Study letters [Snellen equivalent approximately 20/160], with a gain to 63 [19] letters [Snellen equivalent approximately 20/100] at month 12). A total of 19 456 scans underwent complete quantification of IRC and SRF. The best correlation with BCVA at baseline was achieved using a coverage-based, foveal area-weighted IRC parameter (R 2 = 0.59; P < .001). The same baseline parameter also predicted BCVA at 12 months (R 2 = 0.21; P = .003). The BCVA gain correlated with IRC decrease in the exponential model (R 2 = 0.40; P < .001) and linear model (R 2 = 0.25; P = .002). No robust associations were found between SRF and baseline BCVA (R 2 = 0.06; P = .14) or BCVA change (R 2 = 0.14; P = .02). CONCLUSIONS AND RELEVANCE In this proof-of-principle study, IRC-derived morphometric variables correlated well with treatment-naive BCVA and BCVA outcomes in antiangiogenic therapy. While IRC reduction was associated with BCVA gains, some IRC-mediated neurosensory damage remained permanent.

Background/aims The purpose of the study was to create a standardised protocol for choroidal thickness measurements and to determine whether choroidal thickness measurements made on images obtained by spectral domain optical coherence tomography (SD-OCT) and swept source (SS-) OCT from patients with healthy retina are interchangeable when performed manually or with an automatic algorithm. Methods 36 grid cell measurements for choroidal thickness for each volumetric scan were obtained, which were measured for SD-OCT and SS-OCT with two methods on 18 eyes of healthy volunteers. Manual segmentation by experienced retinal graders from the Vienna Reading Center and automated segmentation on >6300 images of the choroid from both devices were statistically compared. Results Model-based comparison between SD-OCT/SS-OCT showed a systematic difference in choroidal thickness of 16.26±0.725 μm (p<0.001) for manual segmentation and 21.55±0.725 μm (p<0.001) for automated segmentation. Comparison of automated with manual segmentations revealed small differences in thickness of −0.68±0.513 μm (p=0.1833). The correlation coefficients for SD-OCT and SS-OCT measures within eyes were 0.975 for manual segmentation and 0.955 for automatic segmentation. Conclusion Choroidal thickness measurements of SD-OCT and SS-OCT indicate that these two devices are interchangeable with a trend of choroidal thickness measurements being slightly thicker on SD-OCT with limited clinical relevance. Use of an automated algorithm to segment choroidal thickness was validated in healthy volunteers.

Automated analyses of large-scale datasets in a cross-sectional study of 1182 patients with active treatment-naïve nAMD demonstrated low spatial correlation of SRF with IRC and PED in contrast to increased colocalization of IRC and PED. These morphological associations may contribute to our understanding of functional deficits in nAMD.

Development of image analysis and machine learning methods for segmentation of clinically significant pathology in retinal spectral-domain optical coherence tomography (SD-OCT), used in disease detection and prediction, is limited due to the availability of expertly annotated reference data. Retinal segmentation methods use datasets that either are not publicly available, come from only one device, or use different evaluation methodologies making them difficult to compare. Thus we present and evaluate a multiple expert annotated reference dataset for the problem of intraretinal cystoid fluid (IRF) segmentation, a key indicator in exudative macular disease. In addition, a standardized framework for segmentation accuracy evaluation, applicable to other pathological structures, is presented. Integral to this work is the dataset used which must be fit for purpose for IRF segmentation algorithm training and testing. We describe here a multivendor dataset comprised of 30 scans. Each OCT scan for system training has been annotated by multiple graders using a proprietary system. Evaluation of the intergrader annotations shows a good correlation, thus making the reproducibly annotated scans suitable for the training and validation of image processing and machine learning based segmentation methods. The dataset will be made publicly available in the form of a segmentation Grand Challenge.

In this exploratory analysis, we describe the characteristics of neuroretinal atrophy in RVO eyes with resolved macular oedema after ranibizumab therapy. Our analysis shows significant, predominantly retinal thinning in the IPL to ONL compartment in focal macular areas in 11% of patients with RVO. Eyes with retinal atrophy did not show poorer BCVA outcomes.

Vitreomacular adhesion (VMA) represents a prognostic biomarker in the management of exudative macular disease using anti-vascular endothelial growth factor (VEGF) agents. However, manual evaluation of VMA in 3D optical coherence tomography (OCT) is laborious and data on its impact on therapy of retinal vein occlusion (RVO) are limited. The aim of this study was to (1) develop a fully automated segmentation algorithm for the posterior vitreous boundary and (2) to study the effect of VMA on anti-VEGF therapy for RVO. A combined machine learning/graph cut segmentation algorithm for the posterior vitreous boundary was designed and evaluated. 391 patients with central/branch RVO under standardized ranibizumab treatment for 6/12 months were included in a systematic post-hoc analysis. VMA (70%) was automatically differentiated from non-VMA (30%) using the developed method combined with unsupervised clustering. In this proof-of-principle study, eyes with VMA showed larger BCVA gains than non-VMA eyes (BRVO: 15 ± 12 vs. 11 ± 11 letters, p = 0.02; CRVO: 18 ± 14 vs. 9 ± 13 letters, p < 0.01) and received a similar number of retreatments. However, this association diminished after adjustment for baseline BCVA, also when using more fine-grained VMA classes. Our study illustrates that machine learning represents a promising path to assess imaging biomarkers in OCT.

Background and Objective To determine optimal image averaging settings for Spectralis optical coherence tomography (OCT) in patients with and without cataract. Study Design/Material and Methods In a prospective study, the eyes were imaged before and after cataract surgery using seven different image averaging settings. Image quality was quantitatively evaluated using signal-to-noise ratio, distinction between retinal layer image intensity distributions, and retinal layer segmentation performance. Measures were compared pre- and postoperatively across different degrees of averaging. Results 13 eyes of 13 patients were included and 1092 layer boundaries analyzed. Preoperatively, increasing image averaging led to a logarithmic growth in all image quality measures up to 96 frames. Postoperatively, increasing averaging beyond 16 images resulted in a plateau without further benefits to image quality. Averaging 16 frames postoperatively provided comparable image quality to 96 frames preoperatively. Conclusion In patients with clear media, averaging 16 images provided optimal signal quality. A further increase in averaging was only beneficial in the eyes with senile cataract. However, prolonged acquisition time and possible loss of details have to be taken into account.