Clinical Application of a New Assessment Tool for Myelopathy Hand Using Virtual Reality

Abstract

Study Design. A cross-sectional study. Objective. To assess the effectiveness of a new assessment tool, myelopathy-hand functional evaluation system (MFES), in evaluating the hand dysfunction of patients with cervical myelopathy in the 10-second grip-and-release test (10 second G-R test). Summary of Background Data. Clumsy fingers movement is a common symptom of myelopathy patients. Evaluating the impaired hand function can provide a strong basis in assessing the severity of myelopathy. Currently, no objective and effective evaluation method is widely accepted in clinical practice. Methods. MFES mainly consists of a pair of wise-gloves and a computer with software. One hundred and ninety-eight consecutive participants were asked to wear the wise-gloves and then perform 10 seconds G-R test. The movements of each finger were recorded by MFES and converted into waveforms. Relevant waveform parameters were measured and analyzed. The Japanese Orthopedics Association (JOA) scores of each patient were marked and the maximum spinal cord compression (MSCC) was measured on midsagittal T2-weighted magnetic resonance imaging (MRI). Results. Myelopathy patients had a lower number of G-R cycles and a longer time per cycle than healthy subjects. There were significant differences in adduction and abduction time in patients with JOA scores greater than 6, but not in healthy subjects and patients with JOA scores less than 6. The waveforms of ulnar three fingers in myelopathy patients were lower and wider than those in healthy individuals. The average ratio value of wave height to wave width (a/b) could quantitatively reflect such differences of waveforms. According to receiver operating characteristic (ROC) curve analysis, the optimal threshold value of the normal average ratio was more than 1.92. The average a/b value was correlated with the JOA scores of the motor function in the upper extremities (r = 0.842). Conclusion. MFES appears to be an objective and quantitative assessment tool for patients with cervical myelopathy. Level of Evidence: 3

Cervical Myelopathy Screening with Machine Learning Algorithm Focusing on Finger Motion Using Noncontact Sensor

“…There are several studies on finger motion analysis using sensors, 48–51 and some of them analyzed the myelopathy hand 12,14–16 . However, they used wearable sensors such as motion capture systems, strain sensor, and bend sensors, and their methods are difficult to apply as a large-scale screening in clinical practice because of the cost of sensors and time-intensive preparation.…”

Quantitative evaluation of abnormal finger movements in myelopathy hand during the grip and release test using gyro sensors

“…Patients with CCM are characterized by an inability to extend their ulnar two or three fingers [ 2 ]. However, recent studies have revealed that the abnormal finger movements in patients with CCM also occur in the radial fingers, including the index finger [ 3 , 24 , 25 ]. Therefore, the significantly lower peak extension angular velocities of both fingers, observed in this study, were consistent with recent studies and could be considered characteristics of myelopathy hand in patients with CCM.…”

“…One recent study quantitatively described the characteristics of myelopathy using hand gloves with bend sensors [ 3 ]. In this previous study, the number of cycles, range of motion of the fingers, and time per cycle were measured, and the authors reported that patients with CCM had fewer cycles and a longer time per cycle than healthy participants.…”

“…Finger motion is often impaired in patients with cervical compressive myelopathy (CCM) [ 1 ]. The abnormal finger movements in patients with CCM are termed myelopathy hand [ 2 ], and result in impaired extension of the ulnar two or three fingers and an inability to grip and release rapidly [ 2 ]; however, these characteristics of myelopathy hand have not been assessed quantitatively [ 3 ].…”

“…While the GRT is useful to evaluate myelopathy hand, it is subject to interobserver variability. Visual observation and manual counting could not guarantee the participants’ correct finger movements, and so the results were not very accurate [ 3 – 5 ]. Additionally, several previous studies have reported that the number of times decreases with age [ 6 , 7 ], suggesting that the GRT is not specific for CCM or age.…”