Quantification of motor symptom progression in Parkinson’s disease (PD) patients is crucial for assessing disease progression and for optimizing therapeutic interventions, such as dopaminergic medications and deep brain stimulation. Cumulative and heuristic clinical experience has identified various clinical signs associated with PD severity, but these are neither objectively quantifiable nor robustly validated. Video-based objective symptom quantification enabled by machine learning (ML) introduces a potential solution. However, video-based diagnostic tools often have implementation challenges due to expensive and inaccessible technology, and typical “black-box” ML implementations are not tailored to be clinically interpretable. Here, we address these needs by releasing a comprehensive kinematic dataset and developing an interpretable video-based framework that predicts high versus low PD motor symptom severity according to MDS-UPDRS Part III metrics. This data driven approach validated and robustly quantified canonical movement features and identified new clinical insights, not previously appreciated as related to clinical severity, including pinkie finger movements and lower limb and axial features of gait. Our framework is enabled by retrospective, single-view, seconds-long videos recorded on consumer-grade devices such as smartphones, tablets, and digital cameras, thereby eliminating the requirement for specialized equipment. Following interpretable ML principles, our framework enforces robustness and interpretability by integrating (1) automatic, data-driven kinematic metric evaluation guided by pre-defined digital features of movement, (2) combination of bi-domain (body and hand) kinematic features, and (3) sparsity-inducing and stability-driven ML analysis with simple-to-interpret models. These elements ensure that the proposed framework quantifies clinically meaningful motor features useful for both ML predictions and clinical analysis.

帕金森病 （PD） 患者运动症状进展的量化对于评估疾病进展和优化治疗干预（如多巴胺能药物和脑深部刺激）至关重要。累积和启发式临床经验已经确定了与 PD 严重程度相关的各种临床体征，但这些既不可客观量化，也未得到有力验证。机器学习 （ML） 支持的基于视频的客观症状量化引入了一种潜在的解决方案。然而，由于技术昂贵且难以获得，基于视频的诊断工具通常存在实施挑战，并且典型的“黑盒”ML 实施并未经过定制以供临床解释。在这里，我们通过发布一个全面的运动学数据集和开发一个可解释的基于视频的框架来满足这些需求，该框架根据 MDS-UPDRS 第 III 部分指标预测高与低 PD 运动症状的严重程度。这种数据驱动的方法验证并稳健地量化了经典运动特征，并确定了新的临床见解，这些见解以前没有被重视为与临床严重程度有关，包括小指运动以及步态的下肢和轴向特征。我们的框架是通过在智能手机、平板电脑和数码相机等消费级设备上录制的回顾性、单视图、秒长的视频来实现的，因此无需专用设备。 遵循可解释的 ML 原则，我们的框架通过集成 （1） 由预定义的运动数字特征指导的自动、数据驱动的运动学指标评估，（2） 双域（身体和手）运动学特征的组合，以及 （3） 稀疏诱导和稳定性驱动的 ML 分析与易于解释的模型。这些要素确保所提出的框架量化了对 ML 预测和临床分析都有用的具有临床意义的运动特征。