背景:帕金森病(PD)的诊断和对其症状的评估需要亲自进行临床检查。需要对PD症状进行远程评估,特别是在2019年冠状病毒大流行等大流行期间。远程评估PD运动障碍的一种潜在方法是基于视频的分析。在这项研究中,我们旨在评估使用卷积神经网络(CNN)模型从步态视频中预测统一帕金森病评定量表(UPDRS)评分的可行性.
方法:我们回顾性地获得了74例PD患者的737个连续步态视频和相应的神经科医生评定的UPDRS评分。我们利用CNN模型来预测总UPDRS第三部分得分和轴性症状的四个子得分(项目27、28、29和30),运动迟缓(项目23、24、25、26和31),刚度(项目22)和震颤(项目20和21)。我们在80%的步态视频上训练了模型,并使用10%的视频作为验证数据集。我们通过比较模型预测得分与神经科医生对其余10%视频(测试数据集)的评分来评估训练模型的预测性能。我们计算了这些分数之间的确定系数(R2),以评估模型的拟合优度。
结果:在测试数据集中,模型预测值和神经科医生额定值之间的R2值,用于总的UPDRS第三部分评分和轴性症状的子评分,运动迟缓,刚性,震颤分别为0.59、0.77、0.56、0.46和0.0。对于症状严重的患者的视频,性能相对较低。
结论:尽管该模型对总UPDRS第三部分得分的预测性能较低,它在预测轴性症状的子评分方面表现出相对较高的性能。该模型大致预测了中度症状患者的总UPDRS第三部分评分,但由于数据有限,严重症状患者的表现较低.需要更大的数据集来提高模型在临床环境中的性能。
BACKGROUND: The diagnosis of Parkinson\'s disease (PD) and evaluation of its symptoms require in-person clinical examination. Remote evaluation of PD symptoms is desirable, especially during a pandemic such as the coronavirus disease 2019 pandemic. One potential method to remotely evaluate PD motor impairments is video-based analysis. In this study, we aimed to assess the feasibility of predicting the Unified Parkinson\'s Disease Rating Scale (UPDRS) score from gait videos using a convolutional neural network (CNN) model.
METHODS: We retrospectively obtained 737 consecutive gait videos of 74 patients with PD and their corresponding neurologist-rated UPDRS scores. We utilized a CNN model for predicting the total UPDRS part III score and four subscores of axial symptoms (items 27, 28, 29, and 30), bradykinesia (items 23, 24, 25, 26, and 31), rigidity (item 22) and tremor (items 20 and 21). We trained the model on 80% of the gait videos and used 10% of the videos as a validation dataset. We evaluated the predictive performance of the trained model by comparing the model-predicted score with the neurologist-rated score for the remaining 10% of videos (test dataset). We calculated the coefficient of determination (R2) between those scores to evaluate the model\'s goodness of fit.
RESULTS: In the test dataset, the R2 values between the model-predicted and neurologist-rated values for the total UPDRS part III score and subscores of axial symptoms, bradykinesia, rigidity, and tremor were 0.59, 0.77, 0.56, 0.46, and 0.0, respectively. The performance was relatively low for videos from patients with severe symptoms.
CONCLUSIONS: Despite the low predictive performance of the model for the total UPDRS part III score, it demonstrated relatively high performance in predicting subscores of axial symptoms. The model approximately predicted the total UPDRS part III scores of patients with moderate symptoms, but the performance was low for patients with severe symptoms owing to limited data. A larger dataset is needed to improve the model\'s performance in clinical settings.