## 2 Methods
### Atrophy Prediction  
2.3.1 Prediction of Future GA and Stargardt Atrophy Regions Using Longitudinal FAF Images (ReConNet)  
The prediction of progression of geographic and Stargardt atrophy using longitudinal FAF images was trained with two steps (initial step of ReConNet1 and final step of ReConNet2; see Figure 3 for details). The initial step consisted of the prediction/generation of FAF image and atrophy regions (size and location) on the FAF image. The input for the neural network was the zero-month, six-month, and twelve-month FAF images, each paired with the manually graded atrophy label for that visit. The outputs were the predicted/generated FAF image and atrophy at eighteen months. In the second step, the generated/predicted FAF image and label were then used in conjunction with the zero-month, six-month, and twelve-month images in the neural network for enhanced atrophy prediction. An example of this process with the input and output at each step is shown in Figure 3.  
Figure 2: The ensemble neural network structure. The OCT feature maps include traditional mean intensity maps and additional minimum intensity, maximum intensity, median intensity, standard deviation, skewness, kurtosis, gray-level entropy, and thickness of the EZ.  
3.2 Prediction of Progression of Stargardt Atrophy Using Longitudinal SD-OCT Images (ReConNet-Ensemble)  
With the ensemble neural network for Stargardt atrophy progression using longitudinal SD-OCT images, a similar process of two steps (ReConNet1-Ensemble and ReConNet2-Ensemble) was performed. Note that for OCT data, we only had data from three patient visits (month 0, month 6, month 12). Instead of using single input of FAF image for each patient visit, in step 1, various enhanced feature maps at each patient visit derived from the zero-month and six-month SD-OCT scans (each again paired with the manually graded atrophy label for that visit) were sent through the ensemble network, producing both a predicted region of atrophy and a predicted enface image. As mentioned, the enhanced features demonstrated rich 3D atrophy information beyond the mean intensity associated features shown in our previous paper [18]. In step 2, rather than attempt to predict the individual twelve-month OCT-derived feature maps, the predicted enface image was used as a stand-in for the twelve-month feature maps and sent through the neural network again in conjunction with the zero-month and six-month feature maps.  
3.3 Prediction of Interval Growth of GA and Stargardt Atrophy Regions Using Longitudinal FAF Images (ReConNet-Interval)  
In this task, the interval growth of GA and Stargardt atrophy between visits was examined. In the zero-month, six-month, and twelve-month FAF images, the areas labeled as atrophy by graders was set to zero, and interval growth labels were paired with each visit. A placeholder label created by inverting the twelve-month atrophy region ground truth was paired with the twelve-month FAF image. The neural network, ReConNet-Interval, was trained to identify only the interval growth of the region of atrophy. Note that different  
Figure 3: A schematic of the prediction algorithm, where i in the subscript indicates the initial prediction results from ReconNet1 and f in the subscript indicates the final prediction results from ReConNet2.  
from ReConNet and ReConNet-Ensemble, which were performed by two steps where the generated/predicted image and label were used in conjunction with the zero-month, six-month, and twelve-month images in the neural network for enhanced atrophy prediction, the ReConNet-Interval can only be performed for the first step, as the second step will need an additional visit after 18 months to obtain the interval. Hence, the performance of the one step ReConNet1-Interval may not be comparable with the enhanced atrophy prediction results after two steps from ReConNet2 and ReConNet2-Ensemble.