Objective: To investigate the clinical utility of deep convolutional neural network (DCNN) tract classification as a new imaging tool in the preoperative evaluation of children with focal epilepsy (FE).
Methods: A DCNN tract classification deeply learned spatial trajectories of DWI white matter pathways linking electrical stimulation mapping (ESM) findings from 89 children with FE, and then automatically identified white matter pathways associated with eloquent functions (i.e., primary motor, language, and vision). Clinical utility was examined by 1) measuring the nearest distance between DCNN-determined pathways and ESM, 2) evaluating the effectiveness of DCNN-determined pathways to optimize surgical margins via Kalman filter analysis, and 3) evaluating how accurately changes in DCNN-determined language pathway volume can predict changes in language ability via canonical correlation analysis.
Results: DCNN tract classification outperformed other existing methods, achieving an excellent accuracy of 98 % while non-invasively detecting eloquent areas within the spatial resolution of ESM (i.e., 1 cm). The Kalman filter analysis found that the preservation of brain areas within a surgical margin determined by DCNN tract classification predicted lack of postoperative deficit with a high accuracy of 92 %. Postoperative change of DCNN-determined language pathway volume showed a significant correlation with postoperative changes in language ability (R = 0.7, p 0.001).
Conclusion: Our findings demonstrate that postoperative functional deficits substantially differ according to the extent of resected white matter, and that DCNN tract classification may offer key translational information by identifying these pathways in pediatric epilepsy surgery.
Significance: DCNN tract classification may be an effective tool to improve surgical outcome of children with FE.