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.