Parameter-efficient convolutional neural network for drug treatment outcome studies of pediatric epilepsy.

This research aimed to develop and validate a convolutional neural network leveraging T2-weighted (T2W) and FLAIR MRI images to predict pharmacological treatment outcomes for pediatric epilepsy in tuberous sclerosis complex (TSC) patients. Using the EfficientNet3D-B0 architecture, we developed Efficient Tuberous Sclerosis Complex-Net (eTSC-Net), a weighted ensemble network that trains binary classification models with T2W and T2FLAIR images to differentiate between controlled and non-controlled TSC patients based on one-year post-anti-seizure medication (ASM) outcomes. Of the 95 patients, 39 (41.1%) achieved seizure control, while 56 (58.9%) continued having seizures after one year of ASM treatment. The dataset was split into training (67 patients), validation (9 patients), and test (19 patients) sets. We developed several models, including a baseline ResNet3D and various EfficientNet3D-B0 configurations. The baseline ResNet3D model achieved an AUC of 0.652. All EfficientNet3D-B0 models outperformed the baseline, with the optimized eTSC-Net achieving an AUC of 0.833 in the testing cohort. eTSC-Net can aid clinicians, including epileptologists, neurologists, neurosurgeons, and other physicians caring for TSC patients, by assisting in formulating targeted treatments.