Evaluation of a low-cost training application to train microelectrode recording identification in deep brain stimulation surgeries.

Objective: Deep brain stimulation (DBS) is a surgical technique that alleviates motor symptoms in Parkinson's disease. Surgically implanted microelectrodes stimulate the basal ganglia to improve patients' symptoms. One of the training challenges for neurophysiologists is to identify during surgery the target area of the brain in which the electrodes must be implanted. Identification is based both on visual and auditory inspection of the microelectrode recordings (MERs) as they are lowered through the basal ganglia. We present the preliminary evaluation of DBSTrainer, a novel desktop application to train neurophysiologists in the identification of signals corresponding to different basal structures.

Methods: A pilot study was conducted with neurologists and neurophysiologists at the Hospital Universitario La Paz (Madrid, Spain). After completing a series of tasks with the application, they were asked to complete an evaluation questionnaire. Usability was assessed using the System Usability Scale (SUS). Functionality, contents, and perceived usefulness were assessed using an ad-hoc Likert questionnaire following the e-MIS framework for surgical learning platforms.

Results: 15 volunteers participated in the study. Obtained SUS score was 86.7 ± 0.47. Most positive aspects on functionality were platform design and interactivity. Contents were found realistic and aligned with learning outcomes. Minor problems were identified with signal loading times.

Conclusions: This study provides preliminary evidence on the usefulness of DBSTrainer. It is, to our knowledge, the first Technology Enhanced Learning application to train neurophysiologists outside the operating room, and thus its introduction can have a real impact on patient safety and surgical outcomes.