RGBカメラを用いた頚髄症スクリーニング手法の提案
Video-Based Hand Tracking for Screening Cervical Myelopathy
2021
井原拓哉*，松井良太*，小山恭史*，山田英莉久，山本皓子，塚本和矢，鏑木秀俊，二村昭元，吉井俊貴，大川淳，斎藤英雄，杉浦裕太，藤田浩二（*共同筆頭著者）
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 (*these authors contributed equally)
[Reference /引用はこちら]
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 (*these authors contributed equally). Screening for degenerative cervical myelopathy with the 10-second grip-and-release test using a smartphone and machine learning: A pilot study. DIGITAL HEALTH. 2023;9. [DOI]
本研究では、グーパー運動の様子を録画した映像を、機械学習を用いて分析する頚髄症スクリーニング手法を提案する。机上に置かれたスマートフォンの直上で内蔵カメラで録画する。次に画像処理を用い、この映像から手指の動きを特徴量として抽出する。この特徴量は前処理の後に、サポートベクタマシン（SVM）分類モデルを用いて患者と健常者の 2クラスに分類される。
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.