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Head movement dynamics in dystonia: a multi-centre retrospective study using visual perceptive deep learning
npj Digital Medicine ( IF 12.4 ) Pub Date : 2024-06-18 , DOI: 10.1038/s41746-024-01140-6
Robert Peach 1, 2 , Maximilian Friedrich 1, 3, 4 , Lara Fronemann 1 , Muthuraman Muthuraman 1 , Sebastian R Schreglmann 1 , Daniel Zeller 1 , Christoph Schrader 5 , Joachim K Krauss 6 , Alfons Schnitzler 7 , Matthias Wittstock 8 , Ann-Kristin Helmers 9 , Steffen Paschen 10 , Andrea Kühn 11 , Inger Marie Skogseid 12 , Wilhelm Eisner 13 , Joerg Mueller 14 , Cordula Matthies 1 , Martin Reich 1 , Jens Volkmann 1 , Chi Wang Ip 1
Affiliation  

Dystonia is a neurological movement disorder characterised by abnormal involuntary movements and postures, particularly affecting the head and neck. However, current clinical assessment methods for dystonia rely on simplified rating scales which lack the ability to capture the intricate spatiotemporal features of dystonic phenomena, hindering clinical management and limiting understanding of the underlying neurobiology. To address this, we developed a visual perceptive deep learning framework that utilizes standard clinical videos to comprehensively evaluate and quantify disease states and the impact of therapeutic interventions, specifically deep brain stimulation. This framework overcomes the limitations of traditional rating scales and offers an efficient and accurate method that is rater-independent for evaluating and monitoring dystonia patients. To evaluate the framework, we leveraged semi-standardized clinical video data collected in three retrospective, longitudinal cohort studies across seven academic centres. We extracted static head angle excursions for clinical validation and derived kinematic variables reflecting naturalistic head dynamics to predict dystonia severity, subtype, and neuromodulation effects. The framework was also applied to a fully independent cohort of generalised dystonia patients for comparison between dystonia sub-types. Computer vision-derived measurements of head angle excursions showed a strong correlation with clinically assigned scores. Across comparisons, we identified consistent kinematic features from full video assessments encoding information critical to disease severity, subtype, and effects of neural circuit interventions, independent of static head angle deviations used in scoring. Our visual perceptive machine learning framework reveals kinematic pathosignatures of dystonia, potentially augmenting clinical management, facilitating scientific translation, and informing personalized precision neurology approaches.



中文翻译:


肌张力障碍的头部运动动力学:利用视觉感知深度学习的多中心回顾性研究



肌张力障碍是一种神经运动障碍,其特征是异常的不自主运动和姿势,特别是影响头部和颈部。然而,目前肌张力障碍的临床评估方法依赖于简化的评分量表,缺乏捕捉肌张力障碍现象复杂的时空特征的能力,阻碍了临床管理并限制了对潜在神经生物学的理解。为了解决这个问题,我们开发了一个视觉感知深度学习框架,利用标准临床视频来全面评估和量化疾病状态以及治疗干预措施(特别是深部脑刺激)的影响。该框架克服了传统评定量表的局限性,并提供了一种独立于评定者的高效、准确的方法来评估和监测肌张力障碍患者。为了评估该框架,我们利用了在七个学术中心的三项回顾性纵向队列研究中收集的半标准化临床视频数据。我们提取静态头部角度偏移进行临床验证,并导出反映自然头部动力学的运动学变量,以预测肌张力障碍的严重程度、亚型和神经调节效应。该框架还应用于完全独立的全身性肌张力障碍患者队列,以比较肌张力障碍亚型。计算机视觉衍生的头角偏移测量结果显示与临床分配的分数有很强的相关性。通过比较,我们从完整的视频评估中确定了一致的运动学特征,这些评估编码对疾病严重程度、亚型和神经回路干预效果至关重要的信息,与评分中使用的静态头角偏差无关。 我们的视觉感知机器学习框架揭示了肌张力障碍的运动学病理特征,有可能增强临床管理,促进科学转化,并为个性化精准神经病学方法提供信息。

更新日期:2024-06-18
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