To investigate the clinical utility of deep convolutional neural network (DCNN)-tract-classification in the preoperative evaluation of children with focal epilepsy, DCNN-tract-classification deeply learned spatial trajectories of DWI tracts linking electrical stimulation mapping (ESM) findings, and then used to detect eloquent tracts. We found that the DCNN-tract-classification can achieve an excellent accuracy (98%) to detect eloquent areas. Also, the subsequent Kalman filter analysis showed that the preservation of detected areas predicts no postoperative deficits with a high mean accuracy across different functions (92%). Our findings demonstrate that DCNN-tract-classification may offer vital translational information in pediatric epilepsy surgery.
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