## 1 Introduction  
[PERSON] disease is a recessive inherited disorder that is the most common form of juvenile-onset macular dystrophy, causing progressive damage or degeneration of the macula [1, 2, 3, 4, 5, 6, 7, 8]. Fundus autofluorescence (FAF) imaging and spectral-domain optical coherence tomography (SD-OCT) are two widely accessible imaging modalities that can aid in the diagnosis, monitoring, and classification of Stargardt disease. FAF imaging provides an in vivo assay of the lipofuscin content within the retinal pigment epithelium (RPE) cells. However, this does not provide any direct measure of the anatomical status of the photoreceptors [9]. On the other hand, SD-OCT allows for the three-dimensionalvisualization of the retina's microstructure and the direct evaluation of individual retinal layers, including the photoreceptors and RPE [10; 11].  
In SD-OCT, [PERSON] disease manifests as disruption of the outer retinal layers, namely the photoreceptor inner and outer segments and RPE. At a Retinal Disease Endpoints meeting with the Food and Drug Administration (FDA) in November of 2016, the integrity of the ellipsoid zone (EZ) was proposed as a reliable measure of the anatomic status of the photoreceptors and a suitable regulatory endpoint for therapeutic intervention clinical trials [12].  
[PERSON] disease is currently phenotypically classified by appearance on various imaging modalities, including FAF and SD-OCT [13; 14; 15; 16]. The rate of progression does not specifically factor into these classifications. Similarly, approaches for automated and semiautomated analysis of features associated with [PERSON] disease focus on analysis at a single point in time [17; 18; 19; 20; 21]. Approaches to the automated analysis of [PERSON] disease frequently make use of some variants of U-Nets. U-Nets are a state-of-the-art deep convolutional neural network (CNN) architecture for semantic segmentation [22]. CNN-based models have achieved state-of-the-art performance in several eye-assessment tasks [23; 24; 25; 26; 27]. However, they are difficult to interpret, and it is often unclear how their final decisions are reached, making CNNs something of a black-box representation [28]. An extension of CNNs to address this issue are ensemble CNNs, where multiple neural networks are used together to each handle different inputs. This approach has been used in the segmentation and prediction of geographic atrophy in age-related macular degeneration [29; 30].  
This study, as a preliminary investigation, seeks to use baseline OCT en face feature maps generated from SD-OCT segmentation for the predictive modeling of future atrophy in Stargardt disease using deep convolutional neural networks. Typically, in the field of disease detection and analysis, OCT en face intensity maps are used [30; 31; 32]. In our approach, we apply multiple advanced en face feature maps beyond intensity maps to augment and enhance data with the goal of enhancing the algorithm's performance. This paper represents the first attempt to use such enhanced en face feature maps generated from disease-relevant retinal layers segmented from OCT to predict future Stargardt atrophy. Furthermore, we report an inherently interpretable ensemble neural network architecture designed to allow the determination of what features or combinations of features contribute to the development and progression of atrophy in Stargardt disease.