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Peer-reviewedCC BY-NC-ND 4.0Scientific Reports

Longitudinal digital speech assessment in progressive supranuclear palsy

  • Kyurim Kang1
  • İlkay Yıldız Potter2
  • Ram Kinker Mishra2
  • Rylee Cole2
  • Tejas Pawar2
  • Charlie Weige Zhao3
  • Antje S. Mefferd4
  • Michael de Riesthal4
  • Ashkan Vaziri2
  • Anne-Marie Wills3
  • Alexander Pantelyat1
4 affiliations
  1. 1Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD USA
  2. 2BioSensics LLC, 57 Chapel St, Newton, MA USA
  3. 3Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA USA
  4. 4Department of Hearing and Speech Sciences, Vanderbilt University Medical Center, Nashville, TN USA
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Abstract

Thirty-two native English-speaking individuals with probable progressive supranuclear palsy-Richardson’s syndrome (PSP-RS) were followed for up to 12 months with clinical and digital speech assessments every 3 months. Speech features from reading a standard passage and sustained phonation were related to clinical outcomes using repeated measures correlation over 12 months and used to develop machine learning (ML)-based predictive models. Longitudinal digital speech measures effectively capture the trajectory of disease progression in PSP-RS and demonstrate sensitivity to changes in disease severity over time, supporting their potential utility as scalable, objective, and remotely deployable digital biomarkers for monitoring disease course and evaluating therapeutic response in clinical and research settings. In addition, ML-based models leveraging speech features accurately predicted both concurrent clinical scores and future clinical scores at three months, demonstrating their potential as a prognostic tool capable of forecasting clinical trajectories in individuals with PSP-RS.

Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1038/s41598-026-62999-x.

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Cite

BibTeXRIS
BibTeX
@article{kang2026longitudinal,
  title = {{Longitudinal digital speech assessment in progressive supranuclear palsy}},
  author = {Kyurim Kang and İlkay Yıldız Potter and Ram Kinker Mishra and Rylee Cole and Tejas Pawar and Charlie Weige Zhao and Antje S. Mefferd and Michael de Riesthal and Ashkan Vaziri and Anne-Marie Wills and Alexander Pantelyat},
  journal = {Scientific Reports},
  year = {2026},
  month = jul,
  doi = {10.1038/s41598-026-62999-x},
  url = {https://doi.org/10.1038/s41598-026-62999-x},
  note = {CC BY-NC-ND 4.0},
}
RIS
TY  - JOUR
TI  - Longitudinal digital speech assessment in progressive supranuclear palsy
AU  - Kyurim Kang
AU  - İlkay Yıldız Potter
AU  - Ram Kinker Mishra
AU  - Rylee Cole
AU  - Tejas Pawar
AU  - Charlie Weige Zhao
AU  - Antje S. Mefferd
AU  - Michael de Riesthal
AU  - Ashkan Vaziri
AU  - Anne-Marie Wills
AU  - Alexander Pantelyat
PY  - 2026
DA  - 2026/07/01
JO  - Scientific Reports
DO  - 10.1038/s41598-026-62999-x
UR  - https://doi.org/10.1038/s41598-026-62999-x
AB  - Thirty-two native English-speaking individuals with probable progressive supranuclear palsy-Richardson’s syndrome (PSP-RS) were followed for up to 12 months with clinical and digital speech assessments every 3 months. Speech features from reading a standard passage and sustained phonation were related to clinical outcomes using repeated measures correlation over 12 months and used to develop machine learning (ML)-based predictive models. Longitudinal digital speech measures effectively capture the trajectory of disease progression in PSP-RS and demonstrate sensitivity to changes in disease severity over time, supporting their potential utility as scalable, objective, and remotely deployable digital biomarkers for monitoring disease course and evaluating therapeutic response in clinical and research settings. In addition, ML-based models leveraging speech features accurately predicted both concurrent clinical scores and future clinical scores at three months, demonstrating their potential as a prognostic tool capable of forecasting clinical trajectories in individuals with PSP-RS.  Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1038/s41598-026-62999-x.
SN  - 2045-2322
ER  -