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

A Pilot Study of a Finger Kinematic Parameter-Based Tool for Evaluating Degenerative Cervical Myelopathy

STUDY DESIGN

This is a cross-sectional study.

OBJECTIVE

To evaluate the effectiveness of a novel finger Kinematic Parameter-Based Tool in the grip and release (G&R) test for assessing degenerative cervical myelopathy (DCM).

SUMMARY OF BACKGROUND DATA

The development and progression of DCM symptoms are gradual and obscure. Although previous studies have objectively evaluated hand movements specific to myelopathy using the G&R test, virtual reality, or wearable sensors, these methods have limitations, such as limited discrimination or inconvenience for simple screening. Consequently, there is a need to develop effective screening methods.

MATERIALS AND METHODS

Totally, 297 asymptomatic volunteers and 258 DCM patients were enrolled. This system comprises a wearable acceleration/gyro sensor. The acceleration/gyro sensor was placed on the little finger of the participants to perform 40 cycles of full-range G&R as quickly as possible. The collected data were then transformed into kinematic parameters using sensor-based software and R studio software (version: RStudio 2022.07.2+576, Boston, USA). Gender, age, and body mass index (BMI) subgroups (classified as BMI<18.5-below normal weight; 18.5≤BMI<25-normal weight group; BMI≥25-overweight group) were matched as predictor variables, and 201 pairs were matched. Nonparametric analysis using the Mann-Whitney U test was used for diagnosing the differences between the two groups, and Kruskal-Wallis's test followed by the Mann-Whitney U test was used for analyzing the differences among three different age groups (<40, 41-60, and >60 yr group). The cut-off value of 10s G&R cycles and a combined parameter were determined using receiver operating characteristics curve analysis, area under the curve, and Youden index.

RESULTS

The authors found that little finger kinematic parameters were significantly lower in DCM patients than in asymptomatic participants. The optimal diagnostic indicator appeared to be the average of the top 10 linear accelerations with an area under the curve of 0.923.

CONCLUSION

The Finger Kinematic Test System is an objective, practical, and quantitative utility that appears to have the capacity to diagnose and evaluate the severity of DCM.

LEVEL OF EVIDENCE

3.

Screening for degenerative cervical myelopathy with the 10-second grip-and-release test using a smartphone and machine learning: A pilot study

Objective

Early detection and intervention are essential for the mitigation of degenerative cervical myelopathy (DCM). However, although several screening methods exist, they are difficult to understand for community-dwelling people, and the equipment required to set up the test environment is expensive. This study investigated the viability of a DCM-screening method based on the 10-second grip-and-release test using a machine learning algorithm and a smartphone equipped with a camera to facilitate a simple screening system.

Methods

Twenty-two participants comprising a group of DCM patients and 17 comprising a control group participated in this study. A spine surgeon diagnosed the presence of DCM. Patients performing the 10-second grip-and-release test were filmed, and the videos were analyzed. The probability of the presence of DCM was estimated using a support vector machine algorithm, and sensitivity, specificity, and area under the curve (AUC) were calculated. Two assessments of the correlation between estimated scores were conducted. The first used a random forest regression model and the Japanese Orthopaedic Association scores for cervical myelopathy (C-JOA). The second assessment used a different model, random forest regression, and the Disabilities of the Arm, Shoulder, and Hand (DASH) questionnaire.

Results

The final classification model had a sensitivity of 90.9%, specificity of 88.2%, and AUC of 0.93. The correlations between each estimated score and the C-JOA and DASH scores were 0.79 and 0.67, respectively.

Conclusions

The proposed model could be a helpful screening tool for DCM as it showed excellent performance and high usability for community-dwelling people and non-spine surgeons.

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

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.

Physical Performance Tests in Adult Neck Pain Patients with and without Clinical Myelopathic Signs: A Matched Case-Control Study

Patients with neck pain may experience cervical myelopathy, this may be detected by clinical myelopathic signs, although they did not have any symptom of myelopathy, except having neck pain. Decreasing physical performance is one symptom of cervical myelopathy that can lead to reduced quality of life in the elderly, however, in adult neck pain with clinical myelopathic signs have not been evaluated. Therefore, this research aimed to compare physical performance in two groups of adult patients with neck pain: those with and without clinical myelopathic signs. A total of 52 participants, gender, age, and body mass index (BMI) matched were allocated into 2 groups of 26 subjects with neck pain, those with, and without, clinical myelopathic signs. The grip and release test, nine-hole peg test, ten second step test and foot-tapping test were evaluated. The group of neck pain participants with clinical myelopathic signs exhibited greater impairment in all the tests than the group without clinical myelopathic signs (p < 0.001). Effect sizes (Cohen’s d) were grip and release test: 2.031, nine-hole peg test: 1.143, ten second step test: 1.329, and foot-tapping test: 0.798. Neck pain participants with clinical myelopathic signs demonstrated reduced physical performance. Physical performance tests may need to assessed in adult patients with neck pain who had clinical myelopathic signs.