###### Abstract
Currently, two complement inhibitors, pegectocoplan (Apellis Pharmaceuticals, Waltham, MA, USA) and avacincaptad (Iveric Bio, Parsippany-Troy Hills, NJ, USA), demonstrated positive Phase 3 clinical trial results in treating AMD atrophy and were cleared for clinical use by the FDA. As such, detecting macular atrophy and predicting the expected progression, and selecting the optimal therapy have been topics of pressing need and critical importance. Fundus autofluorescence (FAF) is one commonly used imaging method in evaluating Stargardt disease and AMD [15]. With FAF, lipofuscin build-up can be assessed on the produced image of the retina. While FAF imaging has been widely used for diagnosing and predicting macular atrophy progression, it has notable limitations. FAF provides 2D imaging with restricted depth penetration and specificity. Additionally, the bright blue excitation light used in FAF can cause patient discomfort. In contrast, spectral-domain optical coherence tomography (SD-OCT) is a 3D imaging modality that excels in delivering high-resolution, cross-sectional images of the macula, making it ideal for a detailed tissue analysis in retinal layers [16; 17]. OCT typically operates at wavelengths around 850 nm or 1050 nm, which generally enhances patient comfort. Hence, beyond FAF, it is important to develop effective approaches for diagnosing and predicting macular atrophy progression on OCT. The convolutional neural network (CNN) architecture has been widely used in the task of automated semantic segmentation of Stargardt disease and GA [18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37]. However, current approaches to an automated analysis for macular atrophy generally do not incorporate longitudinal data, focusing on one instant in time. One way to incorporate longitudinal data is through the use of recurrent neural networks, such as the long short-term memory (LSTM) architecture, a modification of the recurrent neural network [38; 39; 40; 41; 42; 43; 44; 45]. The LSTM architecture has been used in medical image analyses for the detection of cardiomegaly, consolidation, pleural effusion, and hiatus hernia on X-ray and for Alzheimer's disease diagnosis [46; 47]. In the field of ophthalmology, LSTMs have been successfully used for assessing glaucoma progression, predicting diabetic retinopathy, and predicting late AMD progression. However, in these cases, the LSTM architecture was used for prediction in the classification, not for prediction for the segmentation, of images. Additionally, it is important to being able to predict (or generate) future images (FAF or OCT enface) as well to assist clinicians for decision-making on treatment by visualizing both the predicted future atrophy regions and the associated generated or predicted images. However, there are no reported studies that can generate or predict future images for both FAF and OCT enface. Moreover, the retinal layers in volumetric SD-OCT images include rich information beyond the generally used image features (original retinal layer mean intensity and thickness features). To take advantage of the rich 3D retinal layer information on SD-OCT images beyond 2D FAF images, ensemble CNNs, an extension of CNNs, could be used, where several neural networks each handle separate inputs of the novel features based on the retinal layer and work together to concurrently predict the output of atrophy progression with the CNN features from the ensemble networks together [18; 29; 30]. Per our knowledge, there is so far only one Stargardt disease progression prediction reported that used a self-attended U-Net to predict atrophy progression at twelve months. It is indeed from our own group [25]. The limitation of our previous algorithm is that it only used baseline FAF images as input without additional longitudinal information. Other work in this area is largely focused on GA prediction. In [PERSON] et al., CNNs were used to extract features from color fundus photographs, and these features were subsequently fed into an LSTM.