Tremor is one of the most common neurological symptoms. Its clinical and neurobiological complexity necessitates novel approaches for granular phenotyping. Instrumented neurophysiological analyses have proven useful, but are highly resource-intensive and lack broad accessibility. In contrast, bedside scores are simple to administer, but lack the granularity to capture subtle but relevant tremor features. We utilise the open-source computer vision pose tracking algorithm Mediapipe to track hands in clinical video recordings and use the resulting time series to compute canonical tremor features. This approach is compared to marker-based 3D motion capture, wrist-worn accelerometry, clinical scoring and a second, specifically trained tremor-specific algorithm in two independent clinical cohorts. These cohorts consisted of 66 patients diagnosed with essential tremor, assessed in different task conditions and states of deep brain stimulation therapy. We find that Mediapipe-derived tremor metrics exhibit high convergent clinical validity to scores (Spearman’s ρ = 0.55–0.86, p≤ .01) as well as an accuracy of up to 2.60 mm (95% CI [−3.13, 8.23]) and ≤0.21 Hz (95% CI [−0.05, 0.46]) for tremor amplitude and frequency measurements, matching gold-standard equipment. Mediapipe, but not the disease-specific algorithm, was capable of analysing videos involving complex configurational changes of the hands. Moreover, it enabled the extraction of tremor features with diagnostic and prognostic relevance, a dimension which conventional tremor scores were unable to provide. Collectively, this demonstrates that current computer vision algorithms can be transformed into an accurate and highly accessible tool for video-based tremor analysis, yielding comparable results to gold standard tremor recordings.

震颤是最常见的神经系统症状之一。其临床和神经生物学的复杂性需要新的颗粒表型分析方法。仪器化神经生理学分析已被证明是有用的，但资源高度密集且缺乏广泛的可及性。相比之下，床边评分易于管理，但缺乏捕捉细微但相关震颤特征的粒度。我们利用开源计算机视觉姿势跟踪算法 Mediapipe 来跟踪临床视频记录中的手部，并使用生成的时间序列来计算典型震颤特征。在两个独立的临床队列中，将该方法与基于标记的 3D 运动捕捉、腕戴式加速度测量、临床评分以及第二个经过专门训练的震颤特异性算法进行了比较。这些队列由 66 名被诊断患有原发性震颤的患者组成，在不同的任务条件和深部脑刺激治疗状态下进行评估。我们发现 Mediapipe 衍生的震颤指标对评分表现出较高的收敛临床有效性（Spearman ρ = 0.55–0.86，p≤ .01）以及高达 2.60 mm 的准确度（95% CI [−3.13, 8.23]）， ≤0.21 Hz (95% CI [−0.05, 0.46])，用于震颤幅度和频率测量，匹配金标准设备。 Mediapipe（但不是针对特定疾病的算法）能够分析涉及复杂的手部配置变化的视频。此外，它还能够提取具有诊断和预后相关性的震颤特征，这是传统震颤评分无法提供的维度。 总的来说，这表明当前的计算机视觉算法可以转变为一种准确且易于访问的工具，用于基于视频的震颤分析，产生与金标准震颤记录相当的结果。