Effects of deep brain stimulation of the subthalamic nucleus on patients with Parkinson's disease: a machine-learning voice analysis
oleh: Antonio Suppa, Antonio Suppa, Francesco Asci, Francesco Asci, Giovanni Costantini, Francesco Bove, Carla Piano, Francesca Pistoia, Francesca Pistoia, Rocco Cerroni, Livia Brusa, Valerio Cesarini, Sara Pietracupa, Sara Pietracupa, Nicola Modugno, Alessandro Zampogna, Patrizia Sucapane, Mariangela Pierantozzi, Tommaso Tufo, Tommaso Tufo, Antonio Pisani, 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
| Format: | Article |
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| Diterbitkan: | Frontiers Media S.A. 2023-10-01 |
Deskripsi
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.