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.

OCT outcomes as biomarkers for disease status, visual function, and prognosis in diabetic macular edema.

OBJECTIVE: To evaluate disorganization of retinal inner layers (DRIL), as detected on spectral-domain optical coherence tomography (OCT) images, as a biomarker for diabetic macular edema (DME) activity, visual function, and prognosis in eyes with DME. DESIGN: Longitudinal prospective. METHODS: Post hoc correlation analyses were performed on data from a phase 2 clinical trial. Seventy-one eyes of 71 patients with treatment-naive DME received either suprachoroidally administered CLS-TA (proprietary formulation of a triamcinolone acetonide injectable suspension) combined with intravitreal aflibercept or intravitreal aflibercept with a sham suprachoroidal injection procedure. DRIL area, maximum horizontal extent of DRIL, ellipsoid zone (EZ) integrity, and the presence and location of subretinal (SRF) and intraretinal fluid (IRF) were evaluated at baseline and week 24 by certified reading centre graders. RESULTS: At baseline, the area and maximum horizontal extent of DRIL were negatively correlated with best-corrected visual acuity (BCVA; r = -0.25, p = 0.05 and r = -0.32, p =0.01, respectively). Mean baseline BCVA progressively worsened with each ordinal drop in EZ integrity, improved with the presence of SRF, and was invariant to the presence of IRF. DRIL area and maximum extent were significantly decreased at week 24 (-3.0 mm2 [p < 0.001] and -775.8 mm [p < 0.001], respectively. At week 24, decreases in the area and maximum horizontal extent of DRIL were positively correlated with increases in BCVA (r = -0.40, p = 0.003 and r = -0.30, p = 0.04). Improvements in BCVA at week 24 were no different between patients showing improvement in EZ, SRF, or IRF and those showing no improvement or worsening from baseline. CONCLUSIONS: DRIL area and DRIL maximum horizontal extent were demonstrated to be novel biomarkers for macular edema status, visual function, and prognosis in eyes with treatment-naive DME.

Progression of PROM1-Associated Retinal Degeneration as Determined by Spectral-Domain Optical Coherence Tomography Over a 24-Month Period.

PURPOSE: To evaluate the progression of atrophy as determined by spectral-domain optical coherence tomography (SD-OCT) in patients with molecularly confirmed PROM1-associated retinal degeneration (RD) over a 24-month period. DESIGN: International, multicenter, prospective case series. METHODS: A total of 13 eyes (13 patients) affected with PROM1-associated RD were enrolled at 5 sites and SD-OCT images were obtained at baseline and after 24 months. Loss of mean thickness (MT) and intact area were estimated after semi-automated segmentation for the following individual retinal layers in the central subfield (CS), inner ring, and outer ring of the ETDRS grid: retinal pigment epithelium (RPE), outer segments (OS), inner segments (IS), outer nuclear layer (ONL), inner retina (IR), and total retina (TR). RESULTS: Statistically significant losses of thickness of RPE and TR were detected in the CS and inner ring and of ONL and IS in the outer ring (all P < .05); a statistically significant decrease in the intact area of RPE and IS was observed in the inner ring, and of ONL in the outer ring (all P < .05); the change in MT and the intact area of the other layers showed a trend of decline over an observational period of 24 months. CONCLUSIONS: Significant thickness losses could be detected in outer retinal layers by SD-OCT over a 24-month period in patients with PROM1-associated retinal degeneration. Loss of thickness and/or intact area of such layers may serve as potential endpoints for clinical trials that aim to slow down the disease progression of PROM1-associated retinal degeneration.

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.

Reverse engineering for reconstructing baseline features of dry age-related macular degeneration in optical coherence tomography.

Age-related macular degeneration (AMD) is the most widespread cause of blindness and the identification of baseline AMD features or biomarkers is critical for early intervention. Optical coherence tomography (OCT) imaging produces a 3D volume consisting of cross sections of retinal tissue while fundus fluorescence (FAF) imaging produces a 2D mapping of retina. FAF has been a good standard for assessing dry AMD late-stage geographic atrophy (GA) while OCT has been used for assessing early AMD biomarkers beyond as well. However, previous approaches in large extent defined AMD features subjectively based on clinicians' observation. Deep learning-an objective artificial intelligence approach, may enable to discover 'true' salient AMD features. We develop a novel reverse engineering approach which bases on the backbone of a fully convolutional neural network to objectively identify and visualize AMD early biomarkers in OCT from baseline exams before significant atrophy occurs. Utilizing manually annotated GA regions on FAF from a follow-up visit as ground truth, we segment GA regions and reconstruct early AMD features in baseline OCT volumes. In this preliminary exploration, compared with ground truth, we achieve baseline GA segmentation accuracy of 0.95 and overlapping ratio of 0.65. The reconstructions consistently highlight that large druse and druse clusters with or without mixed hyper-reflective focus lesion on baseline OCT cause the conversion of GA after 12 months. However, hyper-reflective focus lesions and subretinal drusenoid deposit lesions alone are not seen such conversion after 12 months. Further research with larger dataset would be needed to verify these findings.

Automated segmentation and feature discovery of age-related macular degeneration and Stargardt disease via self-attended neural networks.

Age-related macular degeneration (AMD) and Stargardt disease are the leading causes of blindness for the elderly and young adults respectively. Geographic atrophy (GA) of AMD and Stargardt atrophy are their end-stage outcomes. Efficient methods for segmentation and quantification of these atrophic lesions are critical for clinical research. In this study, we developed a deep convolutional neural network (CNN) with a trainable self-attended mechanism for accurate GA and Stargardt atrophy segmentation. Compared with traditional post-hoc attention mechanisms which can only visualize CNN features, our self-attended mechanism is embedded in a fully convolutional network and directly involved in training the CNN to actively attend key features for enhanced algorithm performance. We applied the self-attended CNN on the segmentation of AMD and Stargardt atrophic lesions on fundus autofluorescence (FAF) images. Compared with a preexisting regular fully convolutional network (the U-Net), our self-attended CNN achieved 10.6% higher Dice coefficient and 17% higher IoU (intersection over union) for AMD GA segmentation, and a 22% higher Dice coefficient and a 32% higher IoU for Stargardt atrophy segmentation. With longitudinal image data having over a longer time, the developed self-attended mechanism can also be applied on the visual discovery of early AMD and Stargardt features.

Artificial intelligence for assessment of Stargardt macular atrophy.

Stargardt disease (also known as juvenile macular degeneration or Stargardt macular degeneration) is an inherited disorder of the retina, which can occur in the eyes of children and young adults. It is the most prevalent form of juvenile-onset macular dystrophy, causing progressive (and often severe) vision loss. Images with Stargardt disease are characterized by the appearance of flecks in early and intermediate stages, and the appearance of atrophy, due to cells wasting away and dying, in the advanced stage. The primary measure of late-stage Stargardt disease is the appearance of atrophy. Fundus autofluorescence is a widely available two-dimensional imaging technique, which can aid in the diagnosis of the disease. Spectral-domain optical coherence tomography, in contrast, provides three-dimensional visualization of the retinal microstructure, thereby allowing the status of the individual retinal layers. Stargardt disease may cause various levels of disruption to the photoreceptor segments as well as other outer retinal layers. In recent years, there has been an exponential growth in the number of applications utilizing artificial intelligence for help with processing such diseases, heavily fueled by the amazing successes in image recognition using deep learning. This review regarding artificial intelligence deep learning approaches for the Stargardt atrophy screening and segmentation on fundus autofluorescence images is first provided, followed by a review of the automated retinal layer segmentation with atrophic-appearing lesions and fleck features using artificial intelligence deep learning construct. The paper concludes with a perspective about using artificial intelligence to potentially find early risk factors or biomarkers that can aid in the prediction of Stargardt disease progression.

Automated detection and classification of early AMD biomarkers using deep learning.

Age-related macular degeneration (AMD) affects millions of people and is a leading cause of blindness throughout the world. Ideally, affected individuals would be identified at an early stage before late sequelae such as outer retinal atrophy or exudative neovascular membranes develop, which could produce irreversible visual loss. Early identification could allow patients to be staged and appropriate monitoring intervals to be established. Accurate staging of earlier AMD stages could also facilitate the development of new preventative therapeutics. However, accurate and precise staging of AMD, particularly using newer optical coherence tomography (OCT)-based biomarkers may be time-intensive and requires expert training which may not feasible in many circumstances, particularly in screening settings. In this work we develop deep learning method for automated detection and classification of early AMD OCT biomarker. Deep convolution neural networks (CNN) were explicitly trained for performing automated detection and classification of hyperreflective foci, hyporeflective foci within the drusen, and subretinal drusenoid deposits from OCT B-scans. Numerous experiments were conducted to evaluate the performance of several state-of-the-art CNNs and different transfer learning protocols on an image dataset containing approximately 20000 OCT B-scans from 153 patients. An overall accuracy of 87% for identifying the presence of early AMD biomarkers was achieved.