Frontiers in Neurology (Oct 2023)

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

  • 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

DOI
https://doi.org/10.3389/fneur.2023.1267360
Journal volume & issue
Vol. 14

Abstract

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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.

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