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.
Lingua originale | English |
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pagine (da-a) | N/A-N/A |
Rivista | Frontiers in Neurology |
Volume | 14 |
DOI | |
Stato di pubblicazione | Pubblicato - 2023 |
Keywords
- Parkinson's disease
- deep brain stimulation
- voice analysis
- subthalamic nucleus
- machine-learning