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<p>The impact of Magnetic Resonance Imaging-guided Focused Ultrasound (MRgFUS) on vocal tremor is largely underexplored. We used artificial intelligence to investigate changes in vocal tremor in ET patients undergoing MRgFUS. Eighty-three controls and ET patients (mean age-SEM 70.0–1.0 years), including 39 patients without and 44 patients with clinically overt voice tremor, were assessed before and 24&#xa0;h after MRgFUS targeting the ventral intermediate nucleus (Vim). Voice recordings were collected to calculate the receiver operating characteristic (ROC) curves, the likelihood ratios (LRs) for clinical-instrumental correlations, and the oscillatory activity peak by means of artificial intelligence algorithms. We found that before and after Vim-MRgFUS, artificial intelligence discriminated voices in ET and controls objectively with high accuracy. Also, we verified that Vim-MRgFUS improved voice tremor in ET. Moreover, the analysis showed positive correlations between LRs and clinical scores of voice tremor. Lastly, Vim-MRgFUS reduced the 4–6&#xa0;Hz oscillatory activity peak in ET patients. Therefore, our observations pave the way for objective voice evaluations in ET patients following Vim-MRgFUS, for telemedicine purposes.</p>

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Focused ultrasound thalamotomy improves voice tremor in essential tremor: objective insight from artificial intelligence

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月4日

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  • Francesca Pistoia,
  • Patrizia Sucapane,
  • Francesco Asci,
  • Gennaro Saporito,
  • Giovanni Costantini,
  • Valerio Cesarini,
  • Giovanni Saggio,
  • Simona Sacco,
  • Antonio Suppa

摘要

The impact of Magnetic Resonance Imaging-guided Focused Ultrasound (MRgFUS) on vocal tremor is largely underexplored. We used artificial intelligence to investigate changes in vocal tremor in ET patients undergoing MRgFUS. Eighty-three controls and ET patients (mean age-SEM 70.0–1.0 years), including 39 patients without and 44 patients with clinically overt voice tremor, were assessed before and 24 h after MRgFUS targeting the ventral intermediate nucleus (Vim). Voice recordings were collected to calculate the receiver operating characteristic (ROC) curves, the likelihood ratios (LRs) for clinical-instrumental correlations, and the oscillatory activity peak by means of artificial intelligence algorithms. We found that before and after Vim-MRgFUS, artificial intelligence discriminated voices in ET and controls objectively with high accuracy. Also, we verified that Vim-MRgFUS improved voice tremor in ET. Moreover, the analysis showed positive correlations between LRs and clinical scores of voice tremor. Lastly, Vim-MRgFUS reduced the 4–6 Hz oscillatory activity peak in ET patients. Therefore, our observations pave the way for objective voice evaluations in ET patients following Vim-MRgFUS, for telemedicine purposes.