Video-Based Hand Tracking for Screening Cervical Myelopathy

Matsui, Ryota; Koyama, Tatsuya; Fujita, Koji; Saitō, Hideo; Sugiura, Yuta

doi:10.1007/978-3-030-90436-4_1

Cited by 3 publications

(1 citation statement)

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“…Modified test protocols using video cameras and other devices have been proposed, 15 , 17 – 22 with reports suggesting that there may be more important variables than the number of motion cycles when determining the myelopathy hand. 15 , 20 , 21 , 23 …”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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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.

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