AUTHOR=Suppa Antonio , Asci Francesco , Costantini Giovanni , Bove Francesco , Piano Carla , Pistoia Francesca , Cerroni Rocco , Brusa Livia , Cesarini Valerio , Pietracupa Sara , Modugno Nicola , Zampogna Alessandro , Sucapane Patrizia , Pierantozzi Mariangela , Tufo Tommaso , Pisani Antonio , Peppe Antonella , Stefani Alessandro , Calabresi Paolo , Bentivoglio Anna Rita , Saggio Giovanni , Lazio DBS Study Group , Altavista Maria Concetta , Calciulli Alessandra , Ciavarro Marco , Cortese Francesca , Daniele Antonio , Biase Alessandro De , D'Ercole Manuela , Biase Lazzaro Di , Giuda Daniela Di , Leo Pietro Di , Genovese Danilo , Imbimbo Isabella , Izzo Alessandro , Liperoti Rosa , Marano Giuseppe , Marano Massimo , Mazza Marianna , Monge Alessandra , Montano Nicola , Orsini Michela , Rigon Leonardo , Rizz Marina , Rocchi Camilla , Saporito Gennaro , Vacca Laura , Viselli Fabio TITLE=Effects of deep brain stimulation of the subthalamic nucleus on patients with Parkinson's disease: a machine-learning voice analysis JOURNAL=Frontiers in Neurology VOLUME=14 YEAR=2023 URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2023.1267360 DOI=10.3389/fneur.2023.1267360 ISSN=1664-2295 ABSTRACT=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.