Cervical Myelopathy Screening with Machine Learning Algorithm Focusing on Finger Motion Using Noncontact Sensor. Issue 2 (15th January 2022) - Koyama, Takafumi; Fujita, Koji; Watanabe, Masaru; Kato, Kaho; Sasaki, Toru; Yoshii, Toshitaka; Nimura, Akimoto; Sugiura, Yuta; Saito, Hideo; Okawa, Atsushi
Abstract : Screening for cervical myelopathy (CM) is important, but previous methods are unsuitable for simple screening. We developed an examination through motion analysis using Leap Motion and a machine learning algorithm that could classify CM with high sensitivity. The examination is simple, inexpensive, noninvasive, noncontact, and useful for screening outside hospitals. Abstract : Study Design: Cross-sectional study. Objective: To develop a binary classification model for cervical myelopathy (CM) screening based on a machine learning algorithm using Leap Motion (Leap Motion, San Francisco, CA), a novel noncontact sensor device. Summary of Background Data: Progress of CM symptoms are gradual and cannot be easily identified by the patients themselves. Therefore, screening methods should be developed for patients of CM before deterioration of myelopathy. Although some studies have been conducted to objectively evaluate hand movements specific to myelopathy using cameras or wearable sensors, their methods are unsuitable for simple screening outside hospitals because of the difficulty in obtaining and installing their equipment and the long examination time. Methods: In total, 50 and 28 participants in the CM and control groups were recruited, respectively. The diagnosis of CM was made by spine surgeons. We developed a desktop system using Leap Motion that recorded 35 parameters of fingertip movements while participants gripped and released their fingers as rapidly as possible. A support vector machine was used to develop the binary classification model, and a multiple linear regression analysis was performed to create regression models to estimate the total Japanese Orthopaedic Association (JOA) score and the JOA score of the motor function of the upper extremity (MU-JOA score). Results: The binary classification model indexes were as follows: sensitivity, 84.0%; specificity, 60.7%; accuracy, 75.6%; area under the curve, 0.85. The Spearman rank correlation coefficient between the estimated score and the total JOA score was 0.44 and that between the estimated score and the MU-JOA score was 0.51. Conclusion: Our binary classification model using a machine learning algorithm and Leap Motion could classify CM with high sensitivity and would be useful for CM screening in daily life before consulting doctors and telemedicine. Level of Evidence: 3