학술논문
Effects of deep brain stimulation of the subthalamic nucleus on patients with Parkinson's disease: a machine-learning voice analysis
Document Type
article
Author
Antonio Suppa; Francesco Asci; Giovanni Costantini; Francesco Bove; Carla Piano; Francesca Pistoia; Rocco Cerroni; Livia Brusa; Valerio Cesarini; Sara Pietracupa; Nicola Modugno; Alessandro Zampogna; Patrizia Sucapane; Mariangela Pierantozzi; Tommaso Tufo; Antonio Pisani; Antonella Peppe; Alessandro Stefani; Paolo Calabresi; Anna Rita Bentivoglio; Giovanni Saggio; Lazio DBS Study Group; Maria Concetta Altavista; Alessandra Calciulli; Marco Ciavarro; Francesca Cortese; Antonio Daniele; Alessandro De Biase; Manuela D'Ercole; Lazzaro Di Biase; Daniela Di Giuda; Pietro Di Leo; Danilo Genovese; Isabella Imbimbo; Alessandro Izzo; Rosa Liperoti; Giuseppe Marano; Massimo Marano; Marianna Mazza; Alessandra Monge; Nicola Montano; Michela Orsini; Leonardo Rigon; Marina Rizz; Camilla Rocchi; Gennaro Saporito; Laura Vacca; Fabio Viselli
Source
Frontiers in Neurology, Vol 14 (2023)
Subject
Language
English
ISSN
1664-2295
Abstract
IntroductionDeep 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 methodsIn 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.ResultsClinically, 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.DiscussionSTN-DBS deteriorates speech in patients with PD, as objectively demonstrated by machine-learning voice analysis.