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Stereotact Funct Neurosurg. 2018;96(3):142-150. doi: 10.1159/000488683. Epub 2018 Jul 20.

Data-Driven Prediction of the Therapeutic Window during Subthalamic Deep Brain Stimulation Surgery.

Author information

1
Department of Neurosurgery, CHU Pontchaillou, Rennes, France.
2
INSERM, LTSI U1099, Faculté de Médecine CS 34317, Rennes, France.
3
University of Rennes 1, Rennes, France.
4
Behavior and Basal Ganglia host team 4712, University of Rennes 1, Rennes, France.
5
Department of Neurology, CHU Pontchaillou, Rennes, France.

Abstract

BACKGROUND:

Moving from awake surgery under local anesthesia to asleep surgery under general anesthesia will require to precisely predict the outcome of deep brain stimulation.

OBJECTIVE:

To propose a data-driven prediction of both the therapeutic effect and side effects of the surgery.

METHODS:

The retrospective intraoperative data from 30 patients operated on in the subthalamic nucleus were used to train an artificial neural network to predict the deep brain stimulation outcome. A leave-one-out validation was undertaken to give a predictive performance that would reflect the performance of the predictive model in clinical practice. Three-dimensional coordinates and the amount of current of the electrodes were used to train the model.

RESULTS:

130 electrode positions were reviewed. The areas under the curve were 0.902 and 0.89 for therapeutic and side effects, respectively. The mean sensitivity and specificity were 93.07% (SD 0.95) and 69.24% (SD 5.27) for the therapeutic effect, 73.47% (SD 10.55) and 91.82% (SD 0.12) for the side effect.

CONCLUSION:

Data-driven prediction could be an additional modality to predict deep brain stimulation outcome. Further validation is needed to precisely use this method for performing surgery under general anesthesia.

KEYWORDS:

Basal ganglia; Computer-assisted surgery; Deep brain stimulation; Functional neurosurgery; Image-guided neurosurgery; Neurosurgery; Signal processing; Stimulation; Subthalamic nucleus

PMID:
30032132
DOI:
10.1159/000488683

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