Early detection of Parkinson's disease: Machine learning-based prediction of UPDRS Part III scores in de novo patients using smartphone assessments.

BackgroundDetecting motor symptoms in Parkinson's disease (PD) at home, especially in the prodromal, is crucial for disease-modifying therapies.ObjectiveTo evaluate the effectiveness of machine learning models using smartphone-based assessments in predicting motor symptoms in untreated de novo PD.MethodsUsing a clinical trial in early de novo patients with PD, the PDAssist smartphone application and machine learning models were investigated for eight motor tasks: resting tremor, postural tremor, finger tapping, facial expressions, rigidity, speech, walking, and pronation/supination to predict motor symptoms of PD as comparing with UPDRS Part III scores.ResultsOur prediction model demonstrated acceptable performance in detecting PD mild symptoms, with accuracy ranging from 0.87 to 0.93 for resting tremor, postural tremor, finger tapping, facial expressions and postural stability, while the rigidity model achieved 0.81 accuracy with a Kappa of 0.74, and the speech model showed 0.79 accuracy and 0.61 Kappa, emphasizing its potential for detecting subtle motor deficits and remote monitoring. External validation confirmed the model's robustness, with significantly higher predicted scores (all tasks) for PD patients (9.45 ± 3.08) compared to healthy controls (3.79 ± 1.99, t = -14.27, p < 0.001), validating its ability to differentiate between the two groups.ConclusionsSmartphone-based assessments effectively discriminate de novo PD patients from controls and monitor motor symptoms in prodromal and early PD patients. Future work will involve expanding patient cohorts and refining algorithms for better generalizability and reliability of self-collected data in home settings.