## 3 Results  
Neural network predictions were evaluated using the pixel-wise accuracy, Dice coefficient, sensitivity, and specificity. The initial and final prediction results for ReConNet are shown in Table 1. The initial and final results for ReConNet-Ensemble are shown in Table 2. The results for ReConNet-Interval are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & & **Accuracy** & **Dice Coefficient** & **Sensitivity** & **Specificity** \\ \hline \multirow{3}{*}{Stargardt Atrophy} & ReConNet1-Initial & 0.919 \(\mid\) 0.904 (0.072) & 0.568 \(\mid\) 0.577 (0.163) & 0.406 \(\mid\) 0.432 (0.163) & 0.998 \(\mid\) 0.996 (0.008) \\  & ReConNet2-Final & 0.980 \(\mid\) 0.973 (0.021) & 0.922 \(\mid\) 0.895 (0.086) & 0.876 \(\mid\) 0.84 (0.120) & 0.998 \(\mid\) 0.996 (0.007) \\  & \(p\)-value & \textless{}0.001 & \textless{}0.001 & \textless{}0.001 & 0.020 \\ \cline{2-6} GA & ReConNet1-Initial & 0.901 \(\mid\) 0.896 (0.060) & 0.867 \(\mid\) 0.827 (0.129) & 0.84 \(\mid\) 0.839 (0.107) & 0.971 \(\mid\) 0.938 (0.089) \\  & ReConNet2-Final & 0.928 \(\mid\) 0.919 (0.042) & 0.893 \(\mid\) 0.864 (0.113) & 0.964 \(\mid\) 0.945 (0.062) & 0.920 \(\mid\) 0.901 (0.077) \\  & \(p\)-value & \textless{}0.001 & \textless{}0.001 & \textless{}0.001 & \textless{}0.001 \\ \hline \end{tabular}
\end{table}
Table 1: Results for ReConNet (Median/Mean (SD)).  
When comparing the performance of the neural network with and without the inclusion of a predicted eighteen-month FAF image, the Wilcoxon ranked-sign test was used due to the non-normal distribution of the data, resulting in \(p\)-values less than 0.05 for all metrics for all neural network configurations for both Stargardt atrophy and GA, except for with specificity in one case. Examples of inputs, output predicted regions of atrophy, and ground truths after ReConNet, ReConNet-Ensemble, and ReConNet-Interval are shown in Figure 4, Figure 5 and Figure 6, respectively. In Figure 4, the ReConNet model is applied to longitudinal FAF images to generate future images and predict the full extent of atrophic regions for both Stargardt atrophy and GA. In Figure 5, the ReConNet-Ensemble model combines features from multiple longitudinal OCT enface maps to generate future images and predict the full extent of Stargardt atrophic regions. Figure 6 demonstrates the use of the ReConNet-Interval model to predict the growth interval, rather than the entire atrophic regions, for both Stargardt atrophy and GA. Each of these algorithms has distinct advantages depending on the imaging modality used (2D FAF with ReConNet or 3D OCT with ReConNet-Ensemble) and the specific treatment goals. For instance, ReConNet-Interval is suited for scenarios where the focus is on controlling the rate of atrophic growth, while ReConNet is useful for assessing whether early atrophic changes have been addressed beyond merely suppressing growth.  
Figure 4: Example results of ReConNet. Input FAF images and labels, initial prediction, final prediction, and ground truth comparison for ReConNet with Stargardt atrophy (**Top**) and GA (**Bottom**).  
Figure 5: Example results after ReConNet-Ensemble. Input OCT feature maps, initial prediction, final prediction, and ground truth comparison for ensemble ReConNet with Stargardt atrophy. Input labels are not pictured.  
Figure 6: Example results after ReConNet1-Interval. (**Top**) Input modified FAF images, labels, interval growth prediction, and ground truth comparison with Stargardt atrophy. (**Middle** and **Bottom**) Input-modified FAF images, labels, interval growth prediction, and ground truth comparison with GA. Eighteen-month FAF images are shown for reference.