High-Dimensional Analysis of Finger Motion and Screening of Cervical Myelopathy With a Noncontact Sensor: Diagnostic Case-Control Study

Takafumi Koyama; Ryota Matsui; Akiko Yamamoto; Eriku Yamada; Mio Norose; Takuya Ibara; Hidetoshi Kaburagi; Akimoto Nimura; Yuta Sugiura; Hideo Saito; Atsushi Okawa; Koji Fujita

doi:10.2196/41327

High-Dimensional Analysis of Finger Motion and Screening of Cervical Myelopathy With a Noncontact Sensor: Diagnostic Case-Control Study

JMIR Biomed Eng. 2022 Oct 3;7(2):e41327. doi: 10.2196/41327.

Authors

Affiliations

¹ Department of Orthopaedic and Spinal Surgery, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan.
² School of Science for Open and Environmental Systems, Graduate School of Science and Technology, Keio University, Kanagawa, Japan.
³ Department of Functional Joint Anatomy, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan.

^# Contributed equally.

PMID: 38875599
PMCID: PMC11041434
DOI: 10.2196/41327

Abstract

Background: Cervical myelopathy (CM) causes several symptoms such as clumsiness of the hands and often requires surgery. Screening and early diagnosis of CM are important because some patients are unaware of their early symptoms and consult a surgeon only after their condition has become severe. The 10-second hand grip and release test is commonly used to check for the presence of CM. The test is simple but would be more useful for screening if it could objectively evaluate the changes in movement specific to CM. A previous study analyzed finger movements in the 10-second hand grip and release test using the Leap Motion, a noncontact sensor, and a system was developed that can diagnose CM with high sensitivity and specificity using machine learning. However, the previous study had limitations in that the system recorded few parameters and did not differentiate CM from other hand disorders.

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.

Keywords: 10-second hand grip and release test; Leap Motion; carpal tunnel syndrome; cervical myelopathy; clinical informatics; clumsiness; diagnosis; diagnostic; high-dimensional analysis; high-dimensional data analysis; high-dimensional statistics; machine learning; model; motion detection; motion sensor; myelopathy; nervous system disease; nervous system disorder; screening; screening system; sensor; spinal cord disease; spinal cord disorder; system validation.

©Takafumi Koyama, Ryota Matsui, Akiko Yamamoto, Eriku Yamada, Mio Norose, Takuya Ibara, Hidetoshi Kaburagi, Akimoto Nimura, Yuta Sugiura, Hideo Saito, Atsushi Okawa, Koji Fujita. Originally published in JMIR Biomedical Engineering (http://biomsedeng.jmir.org), 03.10.2022.