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

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.
Stereotact Funct Neurosurg

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