Heye Kathrin, Li Renjie, Bai Quan, St George Rebecca J, Rudd Kaylee, Huang Guan, Meinders Marjan J, Bloem Bastiaan R, Alty Jane E
Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Nijmegen, the Netherlands; Wicking Dementia Research and Education Centre, University of Tasmania, Hobart, Tasmania, Australia.
Wicking Dementia Research and Education Centre, University of Tasmania, Hobart, Tasmania, Australia; School of ICT, University of Tasmania, Hobart, Tasmania, Australia.
J Neurol Sci. 2024 Nov 15;466:123271. doi: 10.1016/j.jns.2024.123271. Epub 2024 Oct 15.
Current diagnosis and monitoring of Parkinson's disease (PD) is based on subjective clinical assessments. Objective measures of motor functioning could support clinical acumen. Computer vision (CV) technology is a promising contactless technique but requires further validation.
To investigate the performance of CV analysis of clinic-based videos of finger-tapping. Our goals were (i) to distinguish PD from healthy controls (HC), when compared to human raters, (ii) to measure the severity of bradykinesia, and (iii) detect ON/OFF medication state.
Videos of thirty-one persons with PD and forty-nine HC were collected during clinical outpatient visits. Videos were analysed using CV to produce speed, amplitude, rhythm and composite bradykinesia measures. All videos were independently rated by three raters using the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) and Modified Bradykinesia Rating Scale (MBRS). Twenty video pairs were conducted in ON and OFF states. Classification accuracy for PD/HC state and ON/OFF state were measured using the Area under Receiver Operating characteristic curve and a confusion matrix. CV and clinical measures were correlated using Spearman coefficients.
CV classified disease state with higher accuracy than clinical raters (91 % sensitivity; 97 % specificity). CV measures of bradykinesia correlated significantly with clinical ratings: R = 0.740 for MDS-UPDRS, 0.715 for MBRS speed, 0.714 for amplitude and 0.504 for rhythm. CV classified ON/OFF state as accurately as clinical raters.
CV can provide a valid, objective and contactless bradykinesia assessment based on clinically collected videos, which offers promise as a new clinical outcome, including in remote settings.
帕金森病（PD）目前的诊断和监测基于主观临床评估。运动功能的客观测量方法有助于提高临床诊断能力。计算机视觉（CV）技术是一种很有前景的非接触式技术，但需要进一步验证。
研究基于临床视频的手指轻敲动作的CV分析性能。我们的目标是：（i）与人工评估者相比，区分帕金森病患者与健康对照者（HC）；（ii）测量运动迟缓的严重程度；（iii）检测药物开/关状态。
在临床门诊就诊期间收集了31例帕金森病患者和49例健康对照者的视频。使用CV分析视频，以得出速度、幅度、节奏和综合运动迟缓测量值。所有视频均由三名评估者使用运动障碍协会统一帕金森病评定量表（MDS-UPDRS）和改良运动迟缓评定量表（MBRS）进行独立评分。对20对视频进行了开/关状态测试。使用受试者操作特征曲线下面积和混淆矩阵测量帕金森病/健康对照者状态及开/关状态的分类准确率。使用Spearman系数对CV测量值与临床测量值进行相关性分析。
CV对疾病状态的分类准确率高于临床评估者（灵敏度91%；特异度97%）。运动迟缓的CV测量值与临床评分显著相关：MDS-UPDRS的R值为0.740，MBRS速度的R值为0.715，幅度的R值为0.714，节奏的R值为0.504。CV对开/关状态的分类与临床评估者一样准确。
CV可以基于临床收集的视频提供有效、客观且非接触式的运动迟缓评估，有望成为一种新的临床结果指标，包括在远程环境中。
