Table 5. Longitudinal Siamese network (LSN) architecture.

Network module	Specifications
Siamese net	Input: 2 x 78 CT values per subject Number of hidden layers: 4 each branch (fixed for all folds) Number of nodes per layer: 25 to 50 (grid search) Output: distance embedding Number of nodes: 10 to 20 (grid search)
Multiplicative modulation	Input: distance embedding, APOE4 status (0, 1, 2) Number of hidden layers: 1 (fixed for all folds) Number of nodes: 1 (fixed for all folds) Output: modulated distance embedding
Concatenation and prediction	Input: modulated distance embedding, clinical scores, age Number of hidden layers: 1 (fixed for all folds) Number of nodes: 10 to 20 (grid search) Output: trajectory class