Applying Wearable Sensors and Machine Learning to the Diagnostic Challenge of Distinguishing Parkinson’s Disease from Other Forms of Parkinsonism

作者：Rana M. Khalil, Lisa Shulman, Ann L. Gruber‐Baldini, Stephen G. Reich, Joseph M. Savitt, Jeffrey M. Hausdorff, Rainer von Coelln, Michael P. Cummings · 发表于：Biomedicines · 年份：2025 · DOI：10.3390/biomedicines13030572 · 被引用次数：10 · 研究领域：Parkinson's Disease Mechanisms and Treatments、Neurological disorders and treatments、Autism Spectrum Disorder Research

Background/Objectives: Parkinson’s Disease (PD) and other forms of parkinsonism share motor symptoms, including tremor, bradykinesia, and rigidity. The overlap in their clinical presentation creates a diagnostic challenge, as conventional methods rely heavily on clinical expertise, which can be subjective and inconsistent. This highlights the need for objective, data-driven approaches such as machine learning (ML) in this area. However, applying ML to clinical datasets faces challenges such as imbalanced class distributions, small sample sizes for non-PD parkinsonism, and heterogeneity within the non-PD group. Methods: This study analyzed wearable sensor data from 260 PD participants and 18 individuals with etiologically diverse forms of non-PD parkinsonism, which were collected during clinical mobility tasks using a single sensor placed on the lower back. We evaluated the performance of ML models in distinguishing these two groups and identified the most informative mobility tasks for classification. Additionally, we examined the clinical characteristics of misclassified participants and presented case studies of common challenges in clinical practice, including diagnostic uncertainty at the patient’s initial visit and changes in diagnosis over time. We also suggested potential steps to address the dataset challenges which limited the models’ performance. Results: Feature importance analysis revealed the Timed Up and Go (TUG) task as the most informative for classification. ...