## 4 Discussion and Conclusions
In Tables 5 and 6, significant differences can be seen when comparing performance between using one feature and using several features. Furthermore, when using several features, a significant difference is seen in the performances between regions used to generate feature maps. However, notably, when only one feature is used, there is no significant difference between the regions used. This indicates that using additional features accentuates the difference between different regions during analysis. In Figure 7, examples of two of the commonly encountered segmentation errors in this study are shown. In both Figure 7A,B, the predicted atrophy fills the hole that is present in the ground truth. In Figure 7B, the predicted atrophy does not include the satellite areas of atrophy in the ground truth. We suspect that this is because, in general, the atrophic regions in the dataset used are contiguous, central, and elliptical without holes. As such, the neural network has not been trained on enough data to consistently recognize when there is a hole in the atrophic region or when there are regions of atrophy outside of the center of the image. This study is not without limitations. Although this is a larger dataset than most cohorts of Stargardt disease, the sample size is still limited. Due to the relative scarcity of data on Stargardt disease, it is difficult to capture all aspects of the disease within a deep learning model, and human graders struggle with making determinations in the process of grading. This is exemplified in the difficulties faced when segmenting such phenotypes as poorly demarcated questionably decreased autofluorescence [38]. Other limitations include segmentation errors and errors introduced through the registration process, where the registered image may be slightly sheared, rotated, or translated compared to the OCT enface image. Additionally, in comparing the neural network described to other current state-of-the-art neural networks used in retinal segmentation tasks, we found that the computational complexity of our model is relatively high with regard to floating-point operations (FLOPs). This is shown in Table 7. The increased complexity comes from the modular nature of the neural network. The complexity scales linearly from the computational complexity of a single U-Net with the number of features used. several feature maps results in better performance than using only one, each additional feature map increases the computational workload significantly. Therefore, it would be ideal to have a method of selecting the feature maps that contribute the most to the accurate prediction of atrophy. We believe that the design of the ensemble neural network and its inherent interpretability with its separation of input feature maps to individual U-Nets will aid in this task. Other future work would include examining predictions at eighteenth and later follow-ups and determining at what point, reliable prediction is no longer possible from the baseline images. Additionally, the performance of other architectures could be explored, such as variational auto-encoders to enhance data augmentation, long short-term memory networks to incorporate longitudinal data, and EfficientNet to reduce computational complexity. Furthermore, other imaging modalities, such as FAF and multi- or hyperspectral images could be examined. Multi- and hyperspectral imaging in particular captures more spectral information than imaging modalities like FAF and SD-OCT, and as such may provide further opportunities to identify unique features of disease processes [44; 45]. Lastly, such an approach could also potentially be adapted to the detection of other retinal diseases in OCT with enhanced performance. This study shows promising results for the development of tools for the predicting of the progression of Stargardt disease, providing insight into the potential use of multiple feature maps beyond the traditionally used mean intensity feature maps to improve performance.