Lu Mandy, Poston Kathleen, Pfefferbaum Adolf, Sullivan Edith V, Fei-Fei Li, Pohl Kilian M, Niebles Juan Carlos, Adeli Ehsan
Computer Science Department, Stanford University, Stanford, CA, USA.
School of Medicine, Stanford University, Stanford, CA, USA.
Med Image Comput Comput Assist Interv. 2020 Oct;12263:637-647. doi: 10.1007/978-3-030-59716-0_61. Epub 2020 Sep 29.
Parkinson's disease (PD) is a progressive neurological disorder primarily affecting motor function resulting in tremor at rest, rigidity, bradykinesia, and postural instability. The physical severity of PD impairments can be quantified through the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS), a widely used clinical rating scale. Accurate and quantitative assessment of disease progression is critical to developing a treatment that slows or stops further advancement of the disease. Prior work has mainly focused on dopamine transport neuroimaging for diagnosis or costly and intrusive wearables evaluating motor impairments. For the first time, we propose a computer vision-based model that observes non-intrusive video recordings of individuals, extracts their 3D body skeletons, tracks them through time, and classifies the movements according to the MDS-UPDRS gait scores. Experimental results show that our proposed method performs significantly better than chance and competing methods with an -score of 0.83 and a balanced accuracy of 81%. This is the first benchmark for classifying PD patients based on MDS-UPDRS gait severity and could be an objective biomarker for disease severity. Our work demonstrates how computer-assisted technologies can be used to non-intrusively monitor patients and their motor impairments. The code is available at https://github.com/mlu355/PD-Motor-Severity-Estimation.
帕金森病（PD）是一种进行性神经疾病，主要影响运动功能，导致静止性震颤、僵硬、运动迟缓及姿势不稳。帕金森病损伤的身体严重程度可通过运动障碍协会统一帕金森病评定量表（MDS - UPDRS）进行量化，这是一种广泛使用的临床评定量表。准确且定量地评估疾病进展对于开发减缓或阻止疾病进一步发展的治疗方法至关重要。先前的工作主要集中在用于诊断的多巴胺转运神经成像或评估运动损伤的昂贵且侵入性的可穿戴设备上。我们首次提出了一种基于计算机视觉的模型，该模型观察个体的非侵入性视频记录，提取其3D身体骨骼，随时间跟踪它们，并根据MDS - UPDRS步态评分对运动进行分类。实验结果表明，我们提出的方法表现明显优于随机猜测和其他竞争方法；F1分数为0.83，平衡准确率为81%。这是首个基于MDS - UPDRS步态严重程度对帕金森病患者进行分类的基准，并且可能成为疾病严重程度的客观生物标志物。我们的工作展示了如何使用计算机辅助技术对患者及其运动损伤进行非侵入性监测。代码可在https://github.com/mlu355/PD - Motor - Severity - Estimation获取。
