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Abstract #4587

Deep learning of electrical stimulation mapping-driven DWI tractography to improve preoperative evaluation of pediatric epilepsy surgery

Min-Hee Lee1,2, Nolan O'Hara2,3, Csaba Juhasz1,2,3,4, Eishi Asano1,3,4, and Jeong-Won Jeong1,2,3,4
1Pediatrics, Wayne State University School of Medicine, Detroit, MI, United States, 2Translational Imaging Laboratory, Children's Hospital of Michigan, Detroit, MI, United States, 3Translational Neuroscience Program, Wayne State University School of Medicine, Detroit, MI, United States, 4Neurology, Wayne State University School of Medicine, Detroit, MI, United States

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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