Khalil Rana M, Shulman Lisa M, Gruber-Baldini Ann L, Reich Stephen G, Savitt Joseph M, Hausdorff Jeffrey M, Coelln Rainer von, Cummings Michael P
Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, USA.
Department of Neurology, University of Maryland School of Medicine, Baltimore, MD 21201, USA.
Biomedicines. 2025 Feb 25;13(3):572. doi: 10.3390/biomedicines13030572.
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. 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. Feature importance analysis revealed the Timed Up and Go (TUG) task as the most informative for classification. When using the TUG test alone, the models' performance exceeded that of combining all tasks, achieving a balanced accuracy of 78.2%, which is within 0.2% of the balanced diagnostic accuracy of movement disorder experts. We also identified differences in some clinical scores between the participants correctly and falsely classified by our models. These findings demonstrate the feasibility of using ML and wearable sensors for differentiating PD from other parkinsonian disorders, addressing key challenges in its diagnosis and streamlining diagnostic workflows.
帕金森病（PD）和其他形式的帕金森综合征具有共同的运动症状，包括震颤、运动迟缓及僵硬。由于传统方法严重依赖临床专业知识，而临床专业知识可能具有主观性且不一致，因此它们在临床表现上的重叠带来了诊断挑战。这凸显了在该领域采用客观的、数据驱动方法（如机器学习（ML））的必要性。然而，将ML应用于临床数据集面临诸多挑战，如类别分布不均衡、非PD帕金森综合征的样本量小以及非PD组内的异质性。本研究分析了260名PD参与者和18名患有病因多样的非PD帕金森综合征个体的可穿戴传感器数据，这些数据是在临床移动任务期间使用放置在腰部下方的单个传感器收集的。我们评估了ML模型区分这两组的性能，并确定了用于分类的最具信息性的移动任务。此外，我们检查了误分类参与者的临床特征，并展示了临床实践中常见挑战的案例研究，包括患者初诊时的诊断不确定性以及诊断随时间的变化。我们还提出了应对限制模型性能的数据集挑战的潜在措施。特征重要性分析表明，计时起立行走（TUG）任务对分类最具信息性。仅使用TUG测试时，模型的性能超过了组合所有任务的性能，平衡准确率达到78.2%，与运动障碍专家的平衡诊断准确率相差不超过0.2%。我们还确定了模型正确分类和错误分类的参与者之间在一些临床评分上的差异。这些发现证明了使用ML和可穿戴传感器区分PD与其他帕金森病障碍的可行性，解决了其诊断中的关键挑战并简化了诊断工作流程。
