University of Ljubljana, Faculty of Computer and Information Science, Večna pot 113, Ljubljana, Slovenia.
University of Ljubljana, Faculty of Computer and Information Science, Večna pot 113, Ljubljana, Slovenia.
Artif Intell Med. 2017 Sep;81:54-62. doi: 10.1016/j.artmed.2017.03.011. Epub 2017 Mar 31.
Parkinson's disease (PD) is currently incurable, however proper treatment can ease the symptoms and significantly improve the quality of life of patients. Since PD is a chronic disease, its efficient monitoring and management is very important. The objective of this paper was to investigate the feasibility of using the features and methodology of a spirography application, originally designed to detect early Parkinson's disease (PD) motoric symptoms, for automatically assessing motor symptoms of advanced PD patients experiencing motor fluctuations. More specifically, the aim was to objectively assess motor symptoms related to bradykinesias (slowness of movements occurring as a result of under-medication) and dyskinesias (involuntary movements occurring as a result of over-medication).
This work combined spirography data and clinical assessments from a longitudinal clinical study in Sweden with the features and pre-processing methodology of a Slovenian spirography application. The study involved 65 advanced PD patients and over 30,000 spiral-drawing measurements over the course of three years. Machine learning methods were used to learn to predict the "cause" (bradykinesia or dyskinesia) of upper limb motor dysfunctions as assessed by a clinician who observed animated spirals in a web interface. The classification model was also tested for comprehensibility. For this purpose a visualisation technique was used to present visual clues to clinicians as to which parts of the spiral drawing (or its animation) are important for the given classification.
Using the machine learning methods with feature descriptions and pre-processing from the Slovenian application resulted in 86% classification accuracy and over 0.90 AUC. The clinicians also rated the computer's visual explanations of its classifications as at least meaningful if not necessarily helpful in over 90% of the cases.
The relatively high classification accuracy and AUC demonstrates the usefulness of this approach for objective monitoring of PD patients. The positive evaluation of computer's explanations suggests the potential use of this methodology in a decision support setting.
帕金森病（PD）目前无法治愈，但是适当的治疗可以缓解症状并显著提高患者的生活质量。由于 PD 是一种慢性病，因此对其进行有效的监测和管理非常重要。本文旨在研究使用原本用于检测早期帕金森病（PD）运动症状的肺活量计应用程序的功能和方法来自动评估出现运动波动的晚期 PD 患者的运动症状的可行性。具体来说，目的是客观评估与运动迟缓（药物不足导致的运动缓慢）和运动障碍（药物过量导致的不自主运动）相关的运动症状。
这项工作结合了瑞典纵向临床研究中的肺活量计数据和临床评估，以及斯洛文尼亚肺活量计应用程序的功能和预处理方法。该研究涉及 65 名晚期 PD 患者，在三年的时间内进行了超过 30000 次螺旋绘制测量。使用机器学习方法来学习预测由观察网络界面中动画螺旋的临床医生评估的上肢运动功能障碍的“原因”（运动迟缓或运动障碍）。还对分类模型进行了可理解性测试。为此，使用可视化技术向临床医生展示有关螺旋绘制（或其动画）的哪些部分对给定分类很重要的视觉线索。
使用来自斯洛文尼亚应用程序的机器学习方法和特征描述以及预处理，得到了 86％的分类准确性和超过 0.90 的 AUC。临床医生还对计算机对其分类的视觉解释进行了评分，在超过 90％的情况下，认为其至少有意义，但不一定有帮助。
相对较高的分类准确性和 AUC 表明了这种方法用于 PD 患者客观监测的有用性。对计算机解释的积极评估表明了该方法在决策支持环境中的潜在用途。
