Deep Learning techniques to predict treatment outcomes in newly diagnosed epilepsy

Motivation: Epilepsy is a complex neurological disorder with a high degree of heterogeneity. Selecting the appropriate antiseizure medication(ASM) is a time-consuming trial-and-error process that requires expert knowledge from neurologists. Goal(s): Our goal was to utilise Deep Learning(DL) techniques with neuroimaging information to predict the treatment outcome of ASM. Approach: We developed a DL model that utilises multi-modal information (MRI scans and clinical characteristics) to predict seizure outcomes of initial ASM for patients with newly diagnosed epilepsy. Results: Our model achieved AUROC/AUPRC of 0.72/0.71 respectively in predicting treatment outcomes, demonstrating the potential of brain MRI scans as a biomarker for treatment response. Impact: The model showed promise for development of decision-support systems that could help neurologists select the best ASM, potentially improving treatment outcomes. Clinical translation will require larger datasets and external validation, but this work implies that MRI contains additional prognostic information.