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

Abstract

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.

Deep Learning-Enhanced Hand Grip and Release Test for Degenerative Cervical Myelopathy: Shortening Assessment Duration to 6 Seconds

“…Nevertheless, the limitations, including a small sample size and the reliance on a specialized sensor, constrained the generalizability of their findings. Ibara et al present a novel smartphone-based method for DCM screening, demonstrating high diagnostic accuracy, but with limitations, including a small sample size, which calls for further validation in larger and diverse cohorts to establish clinical utility [ 9 ]. Therefore, the limitations observed in the aforementioned studies encompass issues related to feasibility, measurement variability, complexity, lack of portability, restricted sample sizes, and a dependence on specialized sensors or equipment.…”

“…They employed Leap Motion and achieved commendable results, with a sensitivity of 84.0%, specificity of 60.7%, and an AUC of 0.85. In the study conducted by Ibara et al [ 9 ], a count-based model exhibited a sensitivity of 79.4%, specificity of 86.4%, and an AUC of 0.87. The final proposed model displayed even more impressive results, with a sensitivity of 90.9%, specificity of 88.2%, and an AUC of 0.93.…”

“…Ibara et al present a novel smartphone-based method for DCM screening, demonstrating high diagnostic accuracy, but with limitations, including a small sample size, which calls for further validation in larger and diverse cohorts to establish clinical utility. 9 Therefore, the limitations observed in the aforementioned studies encompass issues related to feasibility, measurement variability, complexity, lack of portability, restricted sample sizes, and a dependence on specialized sensors or equipment. In our study, we exclusively used the front-facing cameras of smartphones for recording the 10-s G&R test and conducted autonomous processing, indicating great potential in remote or in-home screening and monitoring.…”

“…Recently, the study by Ibara et al presented a smartphone-based recording system for the 10-s G&R test, demonstrating its effectiveness in accurately screening for DCM. Nevertheless, given the limited statistical power inherent in such a small sample size, comprising only 22 DCM patients and 17 controls [ 9 ], further research will be crucial to firmly establish the clinical utility and determine its generalizability across heterogeneous DCM populations. Currently, a widely accepted, easily accessible, effective, and replicable evaluation method for clinical application is lacking.…”