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 expen...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2023-06-01
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| Series: | Digital Health |
| Online Access: | https://doi.org/10.1177/20552076231179030 |
| _version_ | 1797810183509901312 |
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| author | Takuya Ibara Ryota Matsui Takafumi Koyama Eriku Yamada Akiko Yamamoto Kazuya Tsukamoto Hidetoshi Kaburagi Akimoto Nimura Toshitaka Yoshii Atsushi Okawa Hideo Saito Yuta Sugiura Koji Fujita |
| author_facet | Takuya Ibara Ryota Matsui Takafumi Koyama Eriku Yamada Akiko Yamamoto Kazuya Tsukamoto Hidetoshi Kaburagi Akimoto Nimura Toshitaka Yoshii Atsushi Okawa Hideo Saito Yuta Sugiura Koji Fujita |
| author_sort | Takuya Ibara |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-13T07:04:24Z |
| format | Article |
| id | doaj.art-5a37b60e06224d25821395c217f8bd19 |
| institution | Directory Open Access Journal |
| issn | 2055-2076 |
| language | English |
| last_indexed | 2024-03-13T07:04:24Z |
| publishDate | 2023-06-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Digital Health |
| spelling | doaj.art-5a37b60e06224d25821395c217f8bd192023-06-06T13:04:03ZengSAGE PublishingDigital Health2055-20762023-06-01910.1177/20552076231179030Screening for degenerative cervical myelopathy with the 10-second grip-and-release test using a smartphone and machine learning: A pilot studyTakuya Ibara0Ryota Matsui1Takafumi Koyama2Eriku Yamada3Akiko Yamamoto4Kazuya Tsukamoto5Hidetoshi Kaburagi6Akimoto Nimura7Toshitaka Yoshii8Atsushi Okawa9Hideo Saito10Yuta Sugiura11Koji Fujita12 Department of Functional Joint Anatomy, Graduate School of Medical and Dental Sciences, , Tokyo, Japan School of Science for Open and Environmental Systems, Graduate School of Science and Technology, , Kanagawa, Japan Department of Orthopaedic and Spinal Surgery, Graduate School of Medical and Dental Sciences, , Tokyo, Japan Department of Orthopaedic and Spinal Surgery, Graduate School of Medical and Dental Sciences, , Tokyo, Japan Department of Orthopaedic and Spinal Surgery, Graduate School of Medical and Dental Sciences, , Tokyo, Japan Department of Orthopaedic and Spinal Surgery, Graduate School of Medical and Dental Sciences, , Tokyo, Japan Department of Orthopaedic and Spinal Surgery, Graduate School of Medical and Dental Sciences, , Tokyo, Japan Department of Functional Joint Anatomy, Graduate School of Medical and Dental Sciences, , Tokyo, Japan Department of Orthopaedic and Spinal Surgery, Graduate School of Medical and Dental Sciences, , Tokyo, Japan Department of Orthopaedic and Spinal Surgery, Graduate School of Medical and Dental Sciences, , Tokyo, Japan School of Science for Open and Environmental Systems, Graduate School of Science and Technology, , Kanagawa, Japan School of Science for Open and Environmental Systems, Graduate School of Science and Technology, , Kanagawa, Japan Department of Functional Joint Anatomy, Graduate School of Medical and Dental Sciences, , Tokyo, JapanObjective 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.https://doi.org/10.1177/20552076231179030 |
| spellingShingle | Takuya Ibara Ryota Matsui Takafumi Koyama Eriku Yamada Akiko Yamamoto Kazuya Tsukamoto Hidetoshi Kaburagi Akimoto Nimura Toshitaka Yoshii Atsushi Okawa Hideo Saito Yuta Sugiura Koji Fujita Screening for degenerative cervical myelopathy with the 10-second grip-and-release test using a smartphone and machine learning: A pilot study Digital Health |
| title | Screening for degenerative cervical myelopathy with the 10-second grip-and-release test using a smartphone and machine learning: A pilot study |
| title_full | Screening for degenerative cervical myelopathy with the 10-second grip-and-release test using a smartphone and machine learning: A pilot study |
| title_fullStr | Screening for degenerative cervical myelopathy with the 10-second grip-and-release test using a smartphone and machine learning: A pilot study |
| title_full_unstemmed | Screening for degenerative cervical myelopathy with the 10-second grip-and-release test using a smartphone and machine learning: A pilot study |
| title_short | Screening for degenerative cervical myelopathy with the 10-second grip-and-release test using a smartphone and machine learning: A pilot study |
| title_sort | screening for degenerative cervical myelopathy with the 10 second grip and release test using a smartphone and machine learning a pilot study |
| url | https://doi.org/10.1177/20552076231179030 |
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