JMIR Publications

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 (10-s) 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-s test using the Leap Motion, a non-contact 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 a few parameters and did not differentiate CM from other hand disorders.

Objective:

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%; AUC, 0.82. The CM/non-CM model indexes were as follows: sensitivity, 71.0%; specificity, 72.8.7%; AUC, 0.74.

Conclusions:

We developed a screening system capable of diagnosing CM with higher sensitivity and specificity. This system can differentiate CM from CTS patients as well as healthy subjects and has the potential to screen for CM in a variety of subjects.