Novel Deep Learning Network Analysis of Electrical Stimulation Mapping-Driven Diffusion MRI Tractography to Improve Preoperative Evaluation of Pediatric Epilepsy.

ABSTRACT:

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

SUBMITTER: Lee MH 

PROVIDER: S-EPMC7598774 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature