An explainable spatial-temporal graphical convolutional network to score freezing of gait in parkinsonian patients.
An explainable spatial-temporal graphical convolutional network to score freezing of gait in parkinsonian patients.
复制标题
一种可解释的时空图形卷积网络，用于对帕金森病患者的步态冻结进行评分。
DOI：
10.1101/2023.01.13.23284535
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
McKay,JLucas
中科院分区：
文献类型：
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作者：
Kwon,Hyeokhyen;Clifford,GariD;Genias,Imari;Bernhard,Doug;Esper,ChristineD;Factor,StewartA;McKay,JLucas
步态冻结（FOG）是一种在帕金森综合征患者中发现的异质性步态障碍，其导致显著的发病率和社会孤立。FOG目前用通常由运动障碍专家执行的量表来测量（即，MDS-FOG-RS），或通过患者完成的问卷（N-FOG-Q），这两种方法都不足以解决疾病的异质性，不适合用于临床试验。本研究的目的是设计一种方法来客观地测量FOG，从而提高我们识别它和准确评估新疗法的能力。我们研究的一个主要创新是，它是同类研究中第一个使用最大样本量（>30小时，N = 57）的研究，以便应用可解释的多任务深度学习模型来量化药物周期过程中的FOG和不同程度的帕金森病严重程度。我们通过多任务学习训练了可解释的深度学习模型，以同时对FOG进行评分（交叉验证F1评分97.6%），确定用药状态（OFF vs. ON左旋多巴;交叉验证F1评分96.8%），并使用左旋多巴激发试验期间N = 57例患者的良好表征样本的运动学数据测量总PD严重程度（MDS-MRS-III评分预测误差≤ 2.7分）。所提出的模型能够解释运动学运动是如何与每个FOG严重程度水平高度一致的特征相关联的，其中运动障碍专家被训练来识别冻结的特征。总的来说，我们证明了深度学习模型在运动学数据中捕获复杂运动模式的能力可以自动客观地对FOG进行高精度评分。这些模型有可能发现新的FOG运动学生物标志物，可用于假设生成和潜在的临床试验结果的措施。
Freezing of gait (FOG) is a poorly understood heterogeneous gait disorder seen in patients with parkinsonism which contributes to significant morbidity and social isolation. FOG is currently measured with scales that are typically performed by movement disorders specialists (i.e., MDS-UPDRS), or through patient completed questionnaires (N-FOG-Q) both of which are inadequate in addressing the heterogeneous nature of the disorder and are unsuitable for use in clinical trials The purpose of this study was to devise a method to measure FOG objectively, hence improving our ability to identify it and accurately evaluate new therapies. A major innovation of our study is that it is the first study of its kind that uses the largest sample size (>30 h, N = 57) in order to apply explainable, multi-task deep learning models for quantifying FOG over the course of the medication cycle and at varying levels of parkinsonism severity. We trained interpretable deep learning models with multi-task learning to simultaneously score FOG (cross-validated F1 score 97.6%), identify medication state (OFF vs. ON levodopa; cross-validated F1 score 96.8%), and measure total PD severity (MDS-UPDRS-III score prediction error ≤ 2.7 points) using kinematic data of a well-characterized sample of N = 57 patients during levodopa challenge tests. The proposed model was able toexplainhow kinematic movements are associated with each FOG severity level that were highly consistent with the features, in which movement disorders specialists are trained to identify as characteristics of freezing. Overall, we demonstrate that deep learning models’ capability to capture complex movement patterns in kinematic data can automatically and objectively score FOG with high accuracy. These models have the potential to discover novel kinematic biomarkers for FOG that can be used for hypothesis generation and potentially as clinical trial outcome measures.