OBJECTIVE The worldwide prevalence of Parkinson's disease is increasing. There is urgent need for new tools to objectively measure the condition. Existing methods to record the cardinal motor feature of the condition, bradykinesia, using wearable sensors or smartphone apps have not reached large-scale, routine use. We evaluate new computer vision (artificial intelligence) technology, DeepLabCut, as a contactless method to quantify measures related to Parkinson's bradykinesia from smartphone videos of finger tapping. METHODS Standard smartphone video recordings of 133 hands performing finger tapping (39 idiopathic Parkinson's patients and 30 controls) were tracked on a frame-by-frame basis with DeepLabCut. Objective computer measures of tapping speed, amplitude and rhythm were correlated with clinical ratings made by 22 movement disorder neurologists using the Modified Bradykinesia Rating Scale (MBRS) and Movement Disorder Society revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS). RESULTS DeepLabCut reliably tracked and measured finger tapping in standard smartphone video. Computer measures correlated well with clinical ratings of bradykinesia (Spearman coefficients): -0.74 speed, 0.66 amplitude, -0.65 rhythm for MBRS; -0.56 speed, 0.61 amplitude, -0.50 rhythm for MDS-UPDRS; -0.69 combined for MDS-UPDRS. All p < .001. CONCLUSION New computer vision software, DeepLabCut, can quantify three measures related to Parkinson's bradykinesia from smartphone videos of finger tapping. Objective 'contactless' measures of standard clinical examinations were not previously possible with wearable sensors (accelerometers, gyroscopes, infrared markers). DeepLabCut requires only conventional video recording of clinical examination and is entirely 'contactless'. This next generation technology holds potential for Parkinson's and other neurological disorders with altered movements.

中文翻译：

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计算机视觉的洞察力：它可以测量帕金森的手指轻敲运动迟缓吗？

目的 帕金森病的全球患病率正在增加。迫切需要新的工具来客观地衡量病情。使用可穿戴传感器或智能手机应用程序记录疾病的主要运动特征的现有方法还没有达到大规模常规使用。我们评估了新的计算机视觉（人工智能）技术 DeepLabCut，它是一种非接触式方法，可从手指敲击的智能手机视频中量化与帕金森氏症运动迟缓相关的措施。方法 使用 DeepLabCut 逐帧跟踪 133 只手进行手指敲击（39 名特发性帕金森氏症患者和 30 名对照者）的标准智能手机视频记录。攻丝速度的客观计算机测量，振幅和节律与 22 名运动障碍神经学家使用改良运动迟缓评定量表 (MBRS) 和运动障碍协会修订版的统一帕金森病评定量表 (MDS-UPDRS) 进行的临床评定相关。结果 DeepLabCut 可靠地跟踪和测量了标准智能手机视频中的手指敲击。计算机测量与运动迟缓的临床评分（Spearman 系数）密切相关：MBRS 的速度为 -0.74，幅度为 0.66，节律为 -0.65；-0.56 速度，0.61 幅度，-0.50 MDS-UPDRS 节奏；-0.69 组合用于 MDS-UPDRS。所有 p < .001。结论 新的计算机视觉软件 DeepLabCut 可以从手指敲击的智能手机视频中量化与帕金森氏症运动迟缓相关的三个指标。客观的“非接触式” 以前无法使用可穿戴传感器（加速度计、陀螺仪、红外标记）进行标准临床检查的测量。DeepLabCut 只需要常规的临床检查视频记录，并且完全是“非接触式”。这种下一代技术具有治疗帕金森氏症和其他运动改变的神经系统疾病的潜力。