Keywords: Neuro, Epilepsy, Prediction of postoperative language improvement in children with epilepsyWe present a novel deep learning-based tract classification to effectively remove false positive tract streamlines from preoperative DWI connectome data of children with medically intractable epilepsy. Compared to the prediction model without the presented classification where uncontrollable false positive tracts significantly limit the accurate prediction of postoperative language improvement using local efficiency values of key hub regions in the receptive and expressive language networks, the prediction model with the presented classification enhanced the accuracy of about 34% up to 100%/88% for the prediction of receptive/expressive language improvement, especially when the local efficiency values were combined with the clinical variables.
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