Yang Yu-Yuan, Ho Ming-Yang, Tai Chung-Hwei, Wu Ruey-Meei, Kuo Ming-Che, Tseng Yufeng Jane
Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, No. 1 Roosevelt Rd. Sec. 4, Taipei, 10617, Taiwan, ROC.
Department of Computer Science and Information Engineering, National Taiwan University, No. 1 Roosevelt Rd. Sec. 4, Taipei, 10617, Taiwan, ROC.
NPJ Digit Med. 2024 Feb 8;7(1):31. doi: 10.1038/s41746-024-01022-x.
The Motor Disorder Society's Unified Parkinson's Disease Rating Scale (MDS-UPDRS) is designed to assess bradykinesia, the cardinal symptoms of Parkinson's disease (PD). However, it cannot capture the all-day variability of bradykinesia outside the clinical environment. Here, we introduce FastEval Parkinsonism ( https://fastevalp.cmdm.tw/ ), a deep learning-driven video-based system, providing users to capture keypoints, estimate the severity, and summarize in a report. Leveraging 840 finger-tapping videos from 186 individuals (103 patients with Parkinson's disease (PD), 24 participants with atypical parkinsonism (APD), 12 elderly with mild parkinsonism signs (MPS), and 47 healthy controls (HCs)), we employ a dilated convolution neural network with two data augmentation techniques. Our model achieves acceptable accuracies (AAC) of 88.0% and 81.5%. The frequency-intensity (FI) value of thumb-index finger distance was indicated as a pivotal hand parameter to quantify the performance. Our model also shows the usability for multi-angle videos, tested in an external database enrolling over 300 PD patients.
运动障碍协会统一帕金森病评定量表（MDS - UPDRS）旨在评估帕金森病（PD）的主要症状——运动迟缓。然而，它无法捕捉临床环境之外运动迟缓的全天变化情况。在此，我们介绍FastEval帕金森症系统（https://fastevalp.cmdm.tw/），这是一个基于深度学习的视频系统，可让用户捕捉关键点、估计严重程度并生成报告。利用来自186个人的840个手指敲击视频（103例帕金森病（PD）患者、24例非典型帕金森症（APD）参与者、12例有轻度帕金森症体征（MPS）的老年人以及47名健康对照（HCs）），我们采用了带有两种数据增强技术的扩张卷积神经网络。我们的模型实现了88.0%和81.5%的可接受准确率（AAC）。拇指 - 食指距离的频率 - 强度（FI）值被确定为量化表现的关键手部参数。我们的模型在一个纳入300多名PD患者的外部数据库中进行测试，还展示了对多角度视频的适用性。
