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<p>Dopamine transporter (DaT) SPECT can confirm dopaminergic deficiency in Parkinson’s disease (PD) but remains costly and inaccessible. We investigated whether brief smartphone-based motor assessments could predict DaT scan results as a scalable alternative. Data from Oxford and Genoa cohorts included individuals with iRBD, PD, and controls. Machine learning models trained on smartphone-derived features classified DaT scan status and predicted striatal binding ratios, compared with MDS-UPDRS-III benchmarks. Among 100 DaT scans, the smartphone-only XGBoost model achieved AUC = 0.80, improving to 0.82 when combined with MDS-UPDRS-III (AUC’s gender-corrected). A simpler logistic regression model performed better with MDS-UPDRS-III alone (AUC = 0.83) versus smartphone features, with slightly higher performance when combined (AUC = 0.85). Regression models predicted binding ratios with modest error (RMSE = 0.49, R² = 0.56). Gait, tremor, and dexterity features were most predictive. These findings support smartphone-based assessments complementing clinical evaluations, though larger independent validation remains essential.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月1日

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  • 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

摘要

Dopamine transporter (DaT) SPECT can confirm dopaminergic deficiency in Parkinson’s disease (PD) but remains costly and inaccessible. We investigated whether brief smartphone-based motor assessments could predict DaT scan results as a scalable alternative. Data from Oxford and Genoa cohorts included individuals with iRBD, PD, and controls. Machine learning models trained on smartphone-derived features classified DaT scan status and predicted striatal binding ratios, compared with MDS-UPDRS-III benchmarks. Among 100 DaT scans, the smartphone-only XGBoost model achieved AUC = 0.80, improving to 0.82 when combined with MDS-UPDRS-III (AUC’s gender-corrected). A simpler logistic regression model performed better with MDS-UPDRS-III alone (AUC = 0.83) versus smartphone features, with slightly higher performance when combined (AUC = 0.85). Regression models predicted binding ratios with modest error (RMSE = 0.49, R² = 0.56). Gait, tremor, and dexterity features were most predictive. These findings support smartphone-based assessments complementing clinical evaluations, though larger independent validation remains essential.