Smartphone-based prediction of dopaminergic deficit in prodromal and manifest Parkinson’s disease - Takeaways - MDSpire

Smartphone-based prediction of dopaminergic deficit in prodromal and manifest Parkinson’s disease

  • By

  • Katarina M. Gunter

  • Karolien Groenewald

  • Timothee Aubourg

  • Christine Lo

  • Jessica Welch

  • Jamil Razzaque

  • Ludo van Hillegondsberg

  • Adriana Nastasa

  • Pietro-Luca Ratti

  • Beatrice Orso

  • Pietro Mattioli

  • Matteo Pardini

  • Stefano Raffa

  • Federico Massa

  • Daniel R. McGowan

  • Kevin M. Bradley

  • Dario Arnaldi

  • Johannes C. Klein

  • Siddharth Arora

  • Michele T. Hu

  • December 1, 2025

  • 0 min

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  • 1

    Smartphone-based motor assessments can predict dopamine transporter (DaT) scan results in Parkinson's disease, offering a scalable alternative to costly imaging.

  • 2

    The smartphone-only XGBoost model achieved an AUC of 0.80 for classifying DaT scan status, improving to 0.82 when combined with MDS-UPDRS-III.

  • 3

    Gait, tremor, and dexterity features were identified as the most predictive in assessing dopaminergic deficiency using smartphone-derived data.

  • 4

    Logistic regression models performed better with MDS-UPDRS-III alone, achieving an AUC of 0.83, and 0.85 when combined with smartphone features.

  • 5

    Findings support the use of smartphone assessments to complement clinical evaluations, though larger independent validation studies are necessary.

Original Source(s)

Smartphone-based prediction of dopaminergic deficit in prodromal and manifest Parkinson’s disease

  • by Katarina M. Gunter, Karolien Groenewald, Timothee Aubourg, Christine Lo, Jessica Welch, Jamil Razzaque, Ludo van Hillegondsberg, Adriana Nastasa, Pietro-Luca Ratti, Beatrice Orso, Pietro Mattioli, Matteo Pardini, Stefano Raffa, Federico Massa, Daniel R. McGowan, Kevin M. Bradley, Dario Arnaldi, Johannes C. Klein, Siddharth Arora, Michele T. Hu

  • December 1, 2025

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