Distinguishing epilepsy drug treatment outcomes is crucial for treating children with tuberous sclerosis complex (TSC). Here, a deep-learning framework named AE-net was proposed to analyze epilepsy drug treatment outcomes using multi-contrast MRI data. Firstly, multi-contrast image-based models were respectively generated using the EfficientNet3D-B0 networks. Then, an averaging ensemble network was created as the final model. The proposed AE-net achieved the best AUC performance of 0.800 and sub-optimal AUC performance of 0.763 in the testing cohort, better than others. And the proposed method can predict epilepsy drug treatment outcomes to help clinical radiologists formulate more targeted treatments in the future.
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