Refining Planning for Stereoelectroencephalography: A Prospective Validation of Spatial Priors for Computer-Assisted Planning With Application of Dynamic Learning.

ABSTRACT: Objective: Stereoelectroencephalography (SEEG) is a procedure in which many electrodes are stereotactically implanted within different regions of the brain to estimate the epileptogenic zone in patients with drug-refractory focal epilepsy. Computer-assisted planning (CAP) improves risk scores, gray matter sampling, orthogonal drilling angles to the skull and intracerebral length in a fraction of the time required for manual planning. Due to differences in planning practices, such algorithms may not be generalizable between institutions. We provide a prospective validation of clinically feasible trajectories using "spatial priors" derived from previous implantations and implement a machine learning classifier to adapt to evolving planning practices. Methods: Thirty-two patient

SUBMITTER: Vakharia VN 

PROVIDER: S-EPMC7380116 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature