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

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.

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Publication Details

Journal
npj Digital Medicine
Published
2025-12-01
DOI
https://doi.org/10.1038/s41746-025-02148-2
Primary Topic
Parkinson's Disease Mechanisms and Treatments
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Stefano Raffa, Siddharth Arora, Alexandrina Năstasă, Jamil Razzaque et al.
npj Digital Medicine
Parkinson's Disease Mechanisms and Treatments
article

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

Stefano Raffa, Siddharth Arora, Alexandrina Năstasă, Jamil Razzaque, Jessica Welch, Johannes Klein, Pietro‐Luca Ratti, Pietro Mattioli, Christine Lo, Daniel R. McGowan, Federico Massa, Matteo Pardini, Timothée Aubourg, Ludo van Hillegondsberg, Kevin M. Bradley, Dario Arnaldi, Beatrice Orso, Katarina Gunter, Karolien Groenewald
article en

Abstract

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.

npj Digital MedicineVol. 8(1)
University of Oxford (GB), Sheffield Teaching Hospitals NHS Foundation Trust (GB), Ospedale Policlinico San Martino (IT), Oxford University Hospitals NHS Trust (GB), Cardiff University (GB), University of Genoa (IT)
National Institute for Health and Care Research, Parkinson's UK
Openalex Percentile: Top 48%
Parkinson's Disease Mechanisms and Treatments
DOIOpen Access PDFOpenAlex
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