论文标题
自动化的机器视觉启用了手工绘制的运动障碍的检测
Automated machine vision enabled detection of movement disorders from hand drawn spirals
论文作者
论文摘要
用于诊断帕金森氏病（PD）和必需震颤（ET）的广泛使用的测试是手绘形状，在该形状中，该分析是由检查神经科医生观察的。该方法是主观的，并且容易在不同的医生中偏见。由于两种疾病的症状的相似之处，它们经常被误诊。试图自动化该过程的研究通常使用数字化输入，在许多临床环境中，平板电脑或专用设备不起作用。这项研究使用扫描的笔和纸图的数据集以及卷积神经网络（CNN）在PD，ET和对照对象之间进行分类。 PD从对照组中的歧视准确性为98.2％。 PD与ET和对照组的歧视准确性为92％。进行了一项消融研究，并指出正确的高参数优化可以提高准确性高达4.33％。最后，研究表明，使用支持CNN的机器视觉系统可以从手动绘制的螺旋形中提供强大而准确的运动障碍检测。
A widely used test for the diagnosis of Parkinson's disease (PD) and Essential tremor (ET) is hand-drawn shapes,where the analysis is observationally performed by the examining neurologist. This method is subjective and is prone to bias amongst different physicians. Due to the similarities in the symptoms of the two diseases, they are often misdiagnosed.Studies which attempt to automate the process typically use digitized input, where the tablet or specialized equipment are not affordable in many clinical settings. This study uses a dataset of scanned pen and paper drawings and a convolutional neural network (CNN) to perform classification between PD, ET and control subjects. The discrimination accuracy of PD from controls was 98.2%. The discrimination accuracy of PD from ET and from controls was 92%. An ablation study was conducted and indicated that correct hyper parameter optimization can increases the accuracy up to 4.33%. Finally, the study indicates the viability of using a CNN-enabled machine vision system to provide robust and accurate detection of movement disorders from hand drawn spirals.
