Refining Planning for Stereoelectroencephalography: A Prospective Validation of Spatial Priors for Computer-Assisted Planning With Application of Dynamic Learning - Vakharia, Vejay N.; Sparks, Rachel E.; Granados, Alejandro; Miserocchi, Anna; McEvoy, Andrew W.; Ourselin, Sebastien; Duncan, John S. (Frontiers in Neurology, 2020)
Objective Stereoelectroencephalography (SEEG) is a procedure in which many electrodes are stereotactically implanted within different regions of the brain to detect the seizure onset zone in patients with drug-refractory focal epilepsy. Computer-assisted planning (CAP) improves risk scores, grey 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 patients underwent consecutive SEEG implantations utilising computer-assisted planning over two years. Implanted electrodes from the first 12 patients (108 electrodes) were used as a training set from which entry and target point spatial priors were generated. CAP was then prospectively performed using the spatial priors in a further test set of 20 patients (210 electrodes). A K-nearest neighbour (K-NN) machine learning classifier was implemented as an adaptive learning method to modify the spatial priors dynamically. Results All of the 318 prospective computer-assisted planned electrodes were implanted without complication. Spatial priors developed from the training set generated clinically feasible trajectories in 79% of the test set. The remaining 21% required entry or target points outside of the spatial priors. The K-NN classifier was able to dynamically model real-time changes in the spatial priors in order to adapt to the evolving planning requirements. Conclusions We provide spatial priors for common SEEG trajectories that prospectively integrate clinically feasible trajectory planning practices from previous SEEG implantations. This allows institutional SEEG experience to be incorporated and used to guide future implantations. The deployment of a K-NN classifier may improve the generalisability of the algorithm by dynamically modifying the spatial priors in real-time as further implantations are performed.