Department of Orthopedic and Spinal Surgery, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), 1-5-45, Yushima, Bunkyo-ku, Tokyo, 113-8519, Japan.
Department of Functional Joint Anatomy, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), 1-5-45, Yushima, Bunkyo-ku, Tokyo, 113-8519, Japan.
Sci Rep. 2023 Jun 20;13(1):10015. doi: 10.1038/s41598-023-37253-3.
Early detection of cervical myelopathy (CM) is important for a favorable outcome, as its prognosis is poor when left untreated. We developed a screening method for CM using machine learning-based analysis of the drawing behavior of 38 patients with CM and 66 healthy volunteers. Using a stylus pen, the participants traced three different shapes displayed on a tablet device. During the tasks, writing behaviors, such as the coordinates, velocity, and pressure of the stylus tip, along with the drawing time, were recorded. From these data, features related to the drawing pressure, and time to trace each shape and combination of shapes were used as training data for the support vector machine, a machine learning algorithm. To evaluate the accuracy, a receiver operating characteristic curve was generated, and the area under the curve (AUC) was calculated. Models with triangular waveforms tended to be the most accurate. The best triangular wave model identified patients with and without CM with 76% sensitivity and 76% specificity, yielding an AUC of 0.80. Our model was able to classify CM with high accuracy and could be applied to the development of disease screening systems useful outside the hospital setting.
早期发现颈椎病（CM）很重要，因为如果不治疗，其预后很差。我们使用基于机器学习的分析方法，对 38 名颈椎病患者和 66 名健康志愿者的绘图行为进行分析，开发了一种 CM 的筛查方法。参与者使用触笔在平板电脑上绘制三个不同的形状。在任务过程中，记录了笔尖的坐标、速度和压力等书写行为，以及绘图时间。从这些数据中，我们选择了与绘图压力、追踪每个形状和组合形状的时间相关的特征作为支持向量机（一种机器学习算法）的训练数据。为了评估准确性，生成了受试者工作特征曲线，并计算了曲线下面积（AUC）。具有三角波形状的模型往往最为准确。最佳的三角波模型可以识别出有和没有 CM 的患者，其敏感性为 76%，特异性为 76%，AUC 为 0.80。我们的模型能够以较高的准确率对 CM 进行分类，并且可以应用于开发在医院环境之外有用的疾病筛查系统。
