OBJECTIVE: This study aims to develop a system that can diagnose CM with higher sensitivity and specificity, and distinguish CM from carpal tunnel syndrome (CTS), a common hand disorder. We then validated the system with a modified Leap Motion that can record the joints of each finger.
METHODS: In total, 31, 27, and 29 participants were recruited into the CM, CTS, and control groups, respectively. We developed a system using Leap Motion that recorded 229 parameters of finger movements while participants gripped and released their fingers as rapidly as possible. A support vector machine was used for machine learning to develop the binary classification model and calculated the sensitivity, specificity, and area under the curve (AUC). We developed two models, one to diagnose CM among the CM and control groups (CM/control model), and the other to diagnose CM among the CM and non-CM groups (CM/non-CM model).
RESULTS: The CM/control model indexes were as follows: sensitivity 74.2%, specificity 89.7%, and AUC 0.82. The CM/non-CM model indexes were as follows: sensitivity 71%, specificity 72.87%, and AUC 0.74.
CONCLUSIONS: We developed a screening system capable of diagnosing CM with higher sensitivity and specificity. This system can differentiate patients with CM from patients with CTS as well as healthy patients and has the potential to screen for CM in a variety of patients.
目的:本研究旨在开发一种能够以更高的灵敏度和特异性诊断CM的系统,并区分CM和腕管综合征(CTS),一种常见的手部疾病.然后,我们使用改进的LeapMotion验证了该系统,该系统可以记录每个手指的关节。
方法:总共,31、27和29名参与者被招募到CM,CTS,和对照组,分别。我们开发了一个使用LeapMotion的系统,该系统记录了229个手指运动参数,而参与者则尽可能快地握住并释放手指。用支持向量机进行机器学习,建立二元分类模型,计算灵敏度,特异性,和曲线下面积(AUC)。我们开发了两种模型,一个在CM和对照组中诊断CM(CM/控制模型),在CM和非CM组中诊断CM(CM/非CM模型)。
结果:CM/对照模型指标如下:灵敏度74.2%,特异性89.7%,和AUC0.82。CM/非CM模型指数如下:灵敏度71%,特异性72.87%,和AUC0.74。
结论:我们开发了一种能够以更高的灵敏度和特异性诊断CM的筛查系统。该系统可以区分患有CM的患者与患有CTS的患者以及健康患者,并且具有在各种患者中筛查CM的潜力。