## 4 Discussion and Conclusions
Significant improvement was observed when comparing neural networks that used only the mean intensity feature map and the ensembled neural networks that used several additional feature maps (\(p<0.05\)). Significant differences in performance were also observed when using the ELM-IRPE region to generate feature maps compared to using the EZ \(\pm\) 5 region (\(p<0.05\)). These differences were observed for both the six-month and twelve-month predictions. The Dice scores achieved are also an improvement from our previous work [35], which reported a 12-month prediction Dice score of 0.76. Notably, our previous work used FAF images, while this work uses en face maps generated from SD-OCT images. The use of SD-OCT images allows us to focus on disease-relevant retinal layers in a way that is not possible with FAF images. This may explain the improved performance. In Tables 1-4, the highest performances are seen in the ELM-EZ, EZ-IRPE, IRPE-ORPE, ELM-IRPE, and E\(Z\pm\) 5 regions, with weaker performance in the ORPE-C-S regions. This falls in line with the pattern of [PERSON] disease in SD-OCT imaging, namely that disruption is frequently seen at the EZ junction and the RPE. Within these five regions, Dice coefficients remain around 0.8 for both the six-month and twelve-month predictions, indicating that, from the baseline images, predictions can be made for at least up to twelve months later, possibly longer. The Dice coefficients and pixel accuracies calculated from the activations of the individual component U-Nets shown in Tables 1-4 can be thought of as a measure of that feature's strength or contribution. In almost all cases, mean intensity performs as a relatively weak feature, with other, less commonly used features frequently standing out as stronger predictors, such as skewness, kurtosis, and standard deviation. Interestingly, the strength of features can differ depending on which region of the retina was used to generate the feature maps and between six-month and twelve-month predictions. For example, when looking at Dice coefficients, for six-month predictions, the ELM-EZ region has kurtosis as the strongest feature, and the ELM-IRPE region has gray-level entropy as the strongest feature, while for twelve-month predictions, the ELM-EZ region's strongest feature changes to skewness but the ELM-IRPE region's strongest feature remains gray-level entropy. This indicates that the optimal feature maps for prediction may differ depending on the retinal region being examined and how far into the future the prediction is being made. Also of note is that when looking at pixel accuracy, different features appear to contribute more strongly than when looking at Dice coefficients; however, because the neural network was trained in part using Dice loss, we believe the Dice coefficients are a better representation of each features relative strength. In Figure 5, it is possible to compare the final prediction of the ensemble neural network to the input feature maps generated from baseline and the FAF and ground truth from follow-up. Of interest in these images is that while minimum intensity appears to be the feature map in which the area of atrophy is least distinguishable, it is not the lowest-performing feature map, as shown in Tables 1-4. This indicates that feature maps may not be able to be treated independently when deciding which feature maps are most contributory. The inclusion of a group of weak-appearing feature maps may outperform a single feature map that appears strong or more clearly defined. In Figure 6, an increase in the predicted area of atrophy can be seen from the six-month to the twelve-month prediction, as indicated by the orange lines. They appear to correspond to the inner and outer borders of a ring that can be seen on the baseline gray-level entropy feature map. This provides evidence that this neural network is capable of predicting up to twelve months into the future and that predictions for different time points are indeed different.