Buckley Christopher, Alcock Lisa, McArdle Ríona, Rehman Rana Zia Ur, Del Din Silvia, Mazzà Claudia, Yarnall Alison J, Rochester Lynn
Institute of Neuroscience/ Institute for Ageing, Newcastle University, Newcastle Upon Tyne NE4 5PL, UK.
Department of Mechanical Engineering, Sheffield University, Sheffield S1 3JD, UK.
Brain Sci. 2019 Feb 6;9(2):34. doi: 10.3390/brainsci9020034.
Quantifying gait and postural control adds valuable information that aids in understanding neurological conditions where motor symptoms predominate and cause considerable functional impairment. Disease-specific clinical scales exist; however, they are often susceptible to subjectivity, and can lack sensitivity when identifying subtle gait and postural impairments in prodromal cohorts and longitudinally to document disease progression. Numerous devices are available to objectively quantify a range of measurement outcomes pertaining to gait and postural control; however, efforts are required to standardise and harmonise approaches that are specific to the neurological condition and clinical assessment. Tools are urgently needed that address a number of unmet needs in neurological practice. Namely, these include timely and accurate diagnosis; disease stratification; risk prediction; tracking disease progression; and decision making for intervention optimisation and maximising therapeutic response (such as medication selection, disease staging, and targeted support). Using some recent examples of research across a range of relevant neurological conditions-including Parkinson's disease, ataxia, and dementia-we will illustrate evidence that supports progress against these unmet clinical needs. We summarise the novel 'big data' approaches that utilise data mining and machine learning techniques to improve disease classification and risk prediction, and conclude with recommendations for future direction.
量化步态和姿势控制可提供有价值的信息，有助于理解以运动症状为主导并导致严重功能障碍的神经疾病。针对特定疾病的临床量表是存在的；然而，它们往往容易受到主观因素的影响，并且在识别前驱队列中的细微步态和姿势损伤以及纵向记录疾病进展时可能缺乏敏感性。有许多设备可用于客观地量化与步态和姿势控制相关的一系列测量结果；然而，需要努力规范和统一针对神经疾病状况和临床评估的特定方法。迫切需要能够满足神经学实践中一些未满足需求的工具。具体而言，这些需求包括及时准确的诊断、疾病分层、风险预测、跟踪疾病进展以及为优化干预措施和最大化治疗反应（如药物选择、疾病分期和针对性支持）进行决策。通过帕金森病、共济失调和痴呆等一系列相关神经疾病的近期研究实例，我们将阐述支持满足这些未满足临床需求进展的证据。我们总结了利用数据挖掘和机器学习技术来改善疾病分类和风险预测的新型“大数据”方法，并对未来方向提出了建议。
