## 4 Discussions  
This paper represents the first attempt to use longitudinal imaging data to predict future regions of macular atrophy in the setting of Stargardt disease using recurrent (LSTM) and concurrent neural network architecture (ReConNet and ReConNet-Ensemble). The Stargardt atrophy progression prediction performance on OCT images is comparably good to that on FAF. These results underscore the potential of SD-OCT for the efficient and practical assessment of atrophy progression in clinical trials and retina clinics, complementing or surpassing the widely used FAF imaging technique. Additionally, the ReConNet is also applied for the GA progression prediction on FAF, demonstrating its feasibility potentially for general macular atrophy progression prediction. For the prediction of atrophy growth in FAF using ReConNet, as shown in Table 1, the initial ReConNet1 predictions achieved a median Dice coefficient of 0.568 for eighteenth predictions of atrophy in the setting of Stargardt atrophy using baseline zero-month, six-month, and twelve-month data. When this initial generated/predicted eighteen-month images and labels were used in conjunction with prior visits of longitudinal data, a median Dice coefficient of 0.922 for ReConNet2 was achieved, showing significant improvement (62% improvement, \(p\) < 0.05). For GA, median Dice coefficients of 0.867 for ReConNet1 and 0.893 for ReConNet2 were achieved with modest enhancement, probably due to the good performance of ReConNet1 and additionally the smaller size of the GA dataset (60 eyes) compared with the Stargardt atrophy dataset (141 eyes); hence, the improvement space is limited. For the prediction of Stargardt atrophy growth in OCT, the ensemble neural network with various OCT feature maps resulted in a similar pattern of performance. Note that the ensemble neural network was only applied on Stargardt atrophy prediction (with month 0 and 6 to predict month 12 since further longitudinal data were not available). Additionally, the longitudinal OCT data for GA patients were not accessible in this study. Using initial zero-month and six-month data, the ensemble neural network achieved a median Dice coefficient of 0.742 for twelve-month predictions in the setting of Stargardt disease (71 eyes) for the initial ReConNet1-Ensemble, and using the initial prediction in conjunction with the prior visits of longitudinal data resulted in a median Dice coefficient of 0.906 for ReConNet2-Ensemble, again showing significant improvement (22% improvement, \(p\) < 0.05). Similar patterns of results were seen with pixel accuracy, sensitivity, and specificity, with the only exception of specificity for the prediction of Stargardt atrophy when using FAF images. In comparison to our previous work, we have achieved higher Dice coefficients for atrophy prediction through the inclusion of longitudinal data. It is worth noting that the prediction results of the Dice coefficient for Stargardt atrophy using OCT based on ReConNet-Ensemble (Table 2 and Figure 5) is comparable to that using FAF based on ReConNet (Table 1 and Figure 4). This finding is critically important. Usually, FAF has been considered as the best imaging modality regarding imaging quality and has been used as a main imaging modality for clinical trials of new atrophy drugs. However, the FAF imaging acquisition process is very uncomfortable for patients, and it is in a two-dimensional imaging modality. OCT imaging is much more tolerable for patients, is a three-dimensional imaging modality, and is becoming the most popular imaging modality in major retina clinics. Hence, our novel recurrent and concurrent neural networks based on multiple advanced enface OCT maps, which reflect higher-order inherent retinal layer features associated with atrophy, provide a new, more efficient, and practical way for the prediction of atrophy progression in clinical trials and clinics.