Effects of deep brain stimulation of the subthalamic nucleus on patients with Parkinson's disease: a machine-learning voice analysis

introduction: deep brain stimulation of the subthalamic nucleus (STN-DBS) can exert relevant effects on the voice of patients with parkinson's disease (PD). In this study, we used artificial intelligence to objectively analyze the voices of PD patients with STN-DBS. materials and methods: In a cross-sectional study, we enrolled 108 controls and 101 patients with PD. the cohort of PD was divided into two groups: the first group included 50 patients with STN-DBS, and the second group included 51 patients receiving the best medical treatment. the voices were clinically evaluated using the unified parkinson's disease rating scale part-III subitem for voice (UPDRS-III-v). we recorded and then analyzed voices using specific machine-learning algorithms. the likelihood ratio (LR) was also calculated as an objective measure for clinical-instrumental correlations. results: clinically, voice impairment was greater in STN-DBS patients than in those who received oral treatment. Using machine learning, we objectively and accurately distinguished between the voices of STN-DBS patients and those under oral treatments. we also found significant clinical-instrumental correlations since the greater the LRs, the higher the UPDRS-III-v scores. discussion: STN-DBS deteriorates speech in patients with PD, as objectively demonstrated by machine-learning voice analysis.