## 4 Discussion and Conclusions  
This paper represents the first attempt to use novel en face feature maps generated from disease-relevant retinal layers segmented in baseline SD-OCT images to predict future Stargardt atrophy. Previous approaches for automated and semiautomated analysis of features associated with Stargardt disease generally focus on analysis at a single point in time [17; 18; 19; 20; 21]. For example, [PERSON] developed an approach using a U-Net-inspired neural network and graph search algorithm to segment the retinal boundaries in SD-OCT images. [PERSON] developed an approach using a U-Net model with ResNet encoding steps to identify hyperoutofluorescent flecks on FAF images. In an extension of [PERSON]'s approach, we developed an approach using U-Net models and graph search algorithms to segment multiple retinal layers and Stargardt features in SD-OCT images. In other previous work, our group used ResNet to develop an automated screening system for Stargardt atrophy and U-Net to segment the atrophic lesions from FAF images. [PERSON] developed an approach to multilabel segmentation of FAF images of Stargardt atrophy, performing automated segmentation of multiple autofluorescence lesion types using a modified U-Net using ResNet encoder blocks. None of these approaches have considered an attempt to predict the progression of Stargardt disease. The one other attempt to predict the progression of Stargardt disease using deep learning methods also comes from our group, in which a self-attended U-Net was developed to predict the progression of atrophy twelve months from a baseline FAF image [35]. The self-attention mechanism used allowed for the identification of early signs of disease progression towards atrophy, capturing \"feature rings\" corresponding with atrophy progression. With all of the previously mentioned methods, FAF images were used. However, FAF images can be thought of as a 2D summary of a 3D structure. Not only is there a loss of data in that transformation, but also, there can be inclusion of data from irrelevant parts of the 3D structure. With SD-OCT images, it is possible to look at the  
Figure 7: Examples of two of the commonly encountered segmentation errors in this study. In both (**A**) and (**B**), the predicted atrophy fills the hole in the ground truth. In (**B**), the predicted atrophy does not include the satellite areas of atrophy in the ground truth. entire 3D structure and selectively decide what to then include in the 2D representation. In the field of the research of a different macular atrophic disease, i.e., geographic atrophy prediction in late-stage age-related macular degeneration, a related problem, [PERSON] et al. developed a model using FAF and SD-OCT en face images to predict annualized atrophy growth rate using the Inception v3 architecture [36]. In their approach, OCT en face images were generated using mean intensity over full, sub-Bruch's membrane, and above-Bruch's membrane areas. However, they did not look at any more specific regions of the retina (e.g., the ellipsoid zone), and the only feature they used to generate en face maps was mean intensity. A final approach to atrophy prediction, again seen in the context of geographic atrophy, is described by [PERSON] et al. [37]. In their approach, longitudinal data are able to be leveraged using bidirectional long short-term memory (LSTM) based prediction module. Their approach made use of the full volumetric OCT data at multiple visits to predict future atrophy, additionally making use of a 3D U-Net to refine the final segmentation. Their approach avoids the potential loss of axial information that comes from the generation of 2D en face maps. The ensemble neural network presented in this paper achieved a median Dice coefficient of 0.830 for six-month predictions of atrophy against the ground truth and 0.828 for twelve-month predictions of atrophy against the ground truth.