Predicting Antiseizure Medication Treatment in Children with Rare Tuberous Sclerosis Complex–Related Epilepsy Using Deep Learning

Study inclusion criteria. Flow chart details the identification of the study cohort. ILAE indicates International League Against Epilepsy.

The applied operations of preprocessing pipeline, training, and evaluation. Schematic of the data preprocessing pipeline (A) and training and evaluation scheme (B). Five models were trained for each technique and used to predict drug-treatment outcome individually. The 5 predictions were averaged to give the final prediction of the model performances.

Statistical analysis of the clinical data set. A and B, Boxplots for continuous variables. A, Age at onset. B, Age at imaging. The horizontal axis represents groups, and the vertical axis represents features. The middle line of the boxplot is the median of the feature data. The upper and lower bounds of the boxplot are the upper and lower quartiles of the feature data, respectively. P values are the results of the Spearman correlation test. C and D, Stacked barplots for categoric variables. C, Infantile spasms. D, ASM numbers (≥3). The horizontal axis represents features (1.0 represents ASM numbers [≥3] or infantile spasms; 0.0 represents ASM numbers [<3] or no infantile spasms), and the vertical axis represents the number of patients in the 2 groups. P values of continuous variables are the results of F-tests, and P values of categoric variables are the results of χ² tests.

Representative images from a child with TSC in the controlled group, a child with TSC in the uncontrolled group, and a healthy child shown on T2WI, FLAIR, and the proposed synthetic FLAIR3 (TSC lesion, white arrow). Controlled group (A) uncontrolled group (B), and healthy child group (C).

The performances of the final T2-weighted, FLAIR, FLAIR3, clinical pediatric data, and the proposed WAE-net models on the test set.