Probing Deposit-Driven Age-Related Macular Degeneration Via Thicknesses of Outer Retinal Bands and Choroid: ALSTAR2 Baseline.

Purpose: We aimed to identify structural differences in normal eyes, early age-related macular degeneration (AMD), and intermediate AMD eyes using optical coherence tomography (OCT) in a well-characterized, large cross-sectional cohort. Methods: Subjects ≥ 60 years with healthy normal eyes, as well as early or intermediate AMD were enrolled in the Alabama Study on Age-related Macular Degeneration 2 (ALSTAR2; NCT04112667). Using Spectralis HRA + OCT2, we obtained macular volumes for each participant. An auto-segmentation software was used to segment six layers and sublayers: photoreceptor inner and outer segments, subretinal drusenoid deposits (SDDs), retinal pigment epithelium + basal lamina (RPE + BL), drusen, and choroid. After manually refining the segmentations of all B-scans, mean thicknesses in whole, central, inner and outer rings of the ETDRS grid were calculated and compared among groups. Results: This study involved 502 patients, 252 were healthy, 147 had early AMD, and 103 had intermediate AMD eyes (per Age-Related Eye Disease Study [AREDS] 9-step). Intermediate AMD eyes exhibited thicker SDD and drusen, thinner photoreceptor inner segments, and RPE compared to healthy and early AMD eyes. They also had thicker photoreceptor outer segments than early AMD eyes. Early AMD eyes had thinner photoreceptor outer segments than normal eyes but a thicker choroid than intermediate AMD eyes. Using the Beckman scale, 42% of the eyes initially classified as early AMD shifted to intermediate AMD, making thickness differences for photoreceptor outer segments and choroid insignificant. Conclusions: With AMD stages, the most consistent structural differences involve appearance of drusen and SDD, followed by RPE + BL thickness, and then thickness of photoreceptor inner and outer segments. Structural changes in the transition from aging to intermediate AMD include alterations in the outer retinal bands, including the appearance of deposits on either side of the RPE.

Recurrent and Concurrent Prediction of Longitudinal Progression of Stargardt Atrophy and Geographic Atrophy.

Stargardt disease and age-related macular degeneration are the leading causes of blindness in the juvenile and geriatric populations, respectively. The formation of atrophic regions of the macula is a hallmark of the end-stages of both diseases. The progression of these diseases is tracked using various imaging modalities, two of the most common being fundus autofluorescence (FAF) imaging and spectral-domain optical coherence tomography (SD-OCT). This study seeks to investigate the use of longitudinal FAF and SD-OCT imaging (month 0, month 6, month 12, and month 18) data for the predictive modelling of future atrophy in Stargardt and geographic atrophy. To achieve such an objective, we develop a set of novel deep convolutional neural networks enhanced with recurrent network units for longitudinal prediction and concurrent learning of ensemble network units (termed ReConNet) which take advantage of improved retinal layer features beyond the mean intensity features. Using FAF images, the neural network presented in this paper achieved mean (± standard deviation, SD) and median Dice coefficients of 0.895 (± 0.086) and 0.922 for Stargardt atrophy, and 0.864 (± 0.113) and 0.893 for geographic atrophy. Using SD-OCT images for Stargardt atrophy, the neural network achieved mean and median Dice coefficients of 0.882 (± 0.101) and 0.906, respectively. When predicting only the interval growth of the atrophic lesions with FAF images, mean (± SD) and median Dice coefficients of 0.557 (± 0.094) and 0.559 were achieved for Stargardt atrophy, and 0.612 (± 0.089) and 0.601 for geographic atrophy. The prediction performance in OCT images is comparably good to that using FAF which opens a new, more efficient, and practical door in the assessment of atrophy progression for clinical trials and retina clinics, beyond widely used FAF. These results are highly encouraging for a high-performance interval growth prediction when more frequent or longer-term longitudinal data are available in our clinics. This is a pressing task for our next step in ongoing research.

Potential Structural Biomarkers in 3D Images Validated by the First Functional Biomarker for Early Age-Related Macular Degeneration - ALSTAR2 Baseline.

Purpose: Lack of valid end points impedes developing therapeutic strategies for early age-related macular degeneration (AMD). Delayed rod-mediated dark adaptation (RMDA) is the first functional biomarker for incident early AMD. The relationship between RMDA and the status of outer retinal bands on optical coherence tomography (OCT) have not been well defined. This study aims to characterize these relationships in early and intermediate AMD. Methods: Baseline data from 476 participants was assessed including eyes with early AMD (n = 138), intermediate AMD (n = 101), and normal aging (n = 237). Participants underwent volume OCT imaging of the macula and rod intercept time (RIT) was measured. The ellipsoid zone (EZ) and interdigitation zone (IZ) on all OCT B-scans of the volumes were segmented. The area of detectable EZ and IZ, and mean thickness of IZ within the Early Treatment Diabetic Retinopathy Study (ETDRS) grid were computed and associations with RIT were assessed by Spearman's correlation coefficient and age adjusted. Results: Delayed RMDA (longer RIT) was most strongly associated with less preserved IZ area (r = -0.591; P < 0.001), followed by decreased IZ thickness (r = -0.434; P < 0.001), and EZ area (r = -0.334; P < 0.001). This correlation between RIT and IZ integrity was not apparent when considering normal eyes alone within 1.5 mm of the fovea. Conclusions: RMDA is correlated with the status of outer retinal bands in early and intermediate AMD eyes, particularly, the status of the IZ. This correlation is consistent with a previous analysis of only foveal B-scans and is biologically plausible given that retinoid availability, involving transfer at the interface attributed to the IZ, is rate-limiting for RMDA.

Potential Structural Biomarkers in 3D Images Validated by the First Functional Biomarker for Early Age-Related Macular Degeneration - ALSTAR2 Baseline.

Purpose: While intermediate and late age-Related Macular Degeneration (AMD) have been widely investigated, rare studies were focused on the pathophysiologic mechanism of early AMD. Delayed rod-mediated dark adaptation (RMDA) is the first functional biomarker for incident early AMD. The status of outer retinal bands on optical coherence tomography (OCT) may be potential imaging biomarkers and the purpose is to investigate the hypothesis that the integrity of interdigitation zone (IZ) may provide insight into the health of photoreceptors and retinal pigment epithelium (RPE) in early AMD. Methods: We establish the structure-function relationship between ellipsoid zone (EZ) integrity and RMDA, and IZ integrity and RMDA in a large-scale OCT dataset from eyes with normal aging (n=237), early AMD (n=138), and intermediate AMD (n=101) by utilizing a novel deep-learning-derived algorithm with manual correction when needed to segment the EZ and IZ on OCT B-scans (57,596 B-scans), and utilizing the AdaptDx device to measure RMDA. Results: Our data demonstrates that slower RMDA is associated with less preserved EZ (r = -0.334; p<0.001) and IZ area (r = -0.591; p<0.001), and decreased IZ thickness (r = -0.434; p<0.001). These associations are not apparent when considering normal eyes alone. Conclusions: The association with IZ area and RMDA in large-scale data is biologically plausible because retinoid availability and transfer at the interface attributed to IZ is rate-limiting for RMDA. This study supports the hypothesis that the IZ integrity provides insight into the health of photoreceptors and RPE in early AMD and is a potential new imaging biomarker.

Automatic Segmentation in Multiple OCT Layers For Stargardt Disease Characterization Via Deep Learning.

Purpose: This study sought to perform automated segmentation of 11 retinal layers and Stargardt-associated features on spectral-domain optical coherence tomography (SD-OCT) images and to analyze differences between normal eyes and eyes diagnosed with Stargardt disease. Methods: Automated segmentation was accomplished through application of the deep learning-shortest path (DL-SP) framework, a shortest path segmentation approach that is enhanced by a deep learning fully convolutional neural network. To compare normal eyes and eyes diagnosed with Stargardt disease, various retinal layer thickness and intensity feature maps associated with the outer retinal layers were generated. Results: The automated DL-SP approach achieved a mean difference within a subpixel accuracy range for all layers when compared to manually traced layers by expert graders. The algorithm achieved mean and absolute mean differences in border positions for Stargardt features of -0.11 ± 4.17 pixels and 1.92 ± 3.71 pixels, respectively. In several of the feature maps generated, the characteristic Stargardt features of flecks and atrophic-appearing lesions were readily visualized. Conclusions: To the best of our knowledge, this is the first automated algorithm for 11 retinal layer segmentation on OCT in eyes with Stargardt disease, and, furthermore, the feature differences found between eyes diagnosed with Stargardt disease and normal eyes may inform new insights and the better understanding of retinal characteristic morphologic changes caused by Stargardt disease. Translational Relevance: The automated algorithm's performance and the feature differences found using the algorithm's segmentation support the future applications of SD-OCT for the quantitative monitoring of Stargardt disease.

Automated Retinal Layer Segmentation Using Graph-based Algorithm Incorporating Deep-learning-derived Information.

Regular drusen, an accumulation of material below the retinal pigment epithelium (RPE), have long been established as a hallmark early feature of nonneovascular age-related macular degeneration (AMD). Advances in imaging have expanded the phenotype of AMD to include another extracellular deposit, reticular pseudodrusen (RPD) (also termed subretinal drusenoid deposits, SDD), which are located above the RPE. We developed an approach to automatically segment retinal layers associated with regular drusen and RPD in spectral domain (SD) optical coherence tomography (OCT) images. More specifically, a shortest-path algorithm enhanced with probability maps generated through a fully convolutional neural network was used to segment drusen and RPD, as well as 11 retinal layers in SD-OCT volumes. This algorithm achieves a mean difference that is within the subpixel accuracy range drusen and RPD, alongside the other 11 retinal layers, highlighting the high robustness of this algorithm for this dataset. To the best of our knowledge, this is the first report of a validated algorithm for the automated segmentation of the retinal layers including early AMD features of RPD and regular drusen separately on SD-OCT images.