Center for Interdisciplinary Research in Biology (CIRB), Collège de France, CNRS, INSERM, Université PSL, Paris, France.
INRIA Paris, MAMBA (Modelling and Analysis in Medical and Biological Applications), Paris, France.
J Parkinsons Dis. 2022;12(7):2211-2222. doi: 10.3233/JPD-223445.
Among motor symptoms of Parkinson's disease (PD), including rigidity and resting tremor, bradykinesia is a mandatory feature to define the parkinsonian syndrome. MDS-UPDRS III is the worldwide reference scale to evaluate the parkinsonian motor impairment, especially bradykinesia. However, MDS-UPDRS III is an agent-based score making reproducible measurements and follow-up challenging.
Using a deep learning approach, we developed a tool to compute an objective score of bradykinesia based on the guidelines of the gold-standard MDS-UPDRS III.
We adapted and applied two deep learning algorithms to detect a two-dimensional (2D) skeleton of the hand composed of 21 predefined points, and transposed it into a three-dimensional (3D) skeleton for a large database of videos of parkinsonian patients performing MDS-UPDRS III protocols acquired in the Movement Disorder unit of Avicenne University Hospital.
We developed a 2D and 3D automated analysis tool to study the evolution of several key parameters during the protocol repetitions of the MDS-UPDRS III. Scores from 2D automated analysis showed a significant correlation with gold-standard ratings of MDS-UPDRS III, measured with coefficients of determination for the tapping (0.609) and hand movements (0.701) protocols using decision tree algorithms. The individual correlations of the different parameters measured with MDS-UPDRS III scores carry meaningful information and are consistent with MDS-UPDRS III guidelines.
We developed a deep learning-based tool to precisely analyze movement parameters allowing to reliably score bradykinesia for parkinsonian patients in a MDS-UPDRS manner.
在帕金森病（PD）的运动症状中，包括僵硬和静止性震颤，运动迟缓是定义帕金森综合征的强制性特征。MDS-UPDRS III 是评估帕金森运动障碍的全球参考量表，特别是运动迟缓。然而，MDS-UPDRS III 是一种基于代理的评分方法，使得可重复性测量和随访具有挑战性。
我们使用深度学习方法开发了一种工具，根据 MDS-UPDRS III 的金标准指南计算运动迟缓的客观评分。
我们改编并应用了两种深度学习算法来检测由 21 个预定义点组成的手部二维（2D）骨架，并将其转换为帕金森病患者在阿维森纳大学医院运动障碍科进行 MDS-UPDRS III 方案时的大型视频数据库的三维（3D）骨架。
我们开发了一种 2D 和 3D 自动分析工具，以研究 MDS-UPDRS III 方案重复期间的几个关键参数的演变。2D 自动分析的分数与 MDS-UPDRS III 的金标准评分显著相关，使用决策树算法对打点（0.609）和手部运动（0.701）方案的评分进行了确定系数测量。使用 MDS-UPDRS III 评分测量的不同参数的个体相关性具有有意义的信息，并且与 MDS-UPDRS III 指南一致。
我们开发了一种基于深度学习的工具，可以精确分析运动参数，以可靠地对帕金森病患者进行 MDS-UPDRS 方式的运动迟缓评分。
