Screening System for CM
手指運動三次元計測による頚髄症スクリーニング手法
Cervical Myelopathy Screening with Machine Learning Algorithm Focusing on Finger Motion Using Non-Contact Sensor
2021
小山恭史,藤田浩二,渡辺昌,加藤花歩,佐々木亨,吉井俊貴,二村昭元,杉浦裕太,斎藤英雄,大川淳
Takafumi Koyama, Koji Fujita, Masaru Watanabe, Kaho Kato, Toru Sasaki, Toshitaka Yoshii, Akimoto Nimura, Yuta Sugiura, Hideo Saito, Atsushi Okawa

Reference / 引用するならこちら
Takafumi Koyama, Koji Fujita, Masaru Watanabe, Kaho Kato, Toru Sasaki, Toshitaka Yoshii, Akimoto Nimura, Yuta Sugiura, Hideo Saito, Atsushi Okawa, Cervical Myelopathy Screening with Machine Learning Algorithm Focusing on Finger Motion Using Non-Contact Sensor, SPINE. [DOI]

頚髄症は頸椎部の変形に伴う脊髄の圧迫により、巧緻運動障害や歩行障害等の症状を引き起こし、日常生活に支障をきたす疾患である。本研究では巧緻運動障害が表出する手の開閉運動に着目し三次元手指計測装置であるLeap Motionで取得した手指運動情報による頚髄症スクリーニングシステムを開発した。取得された手指運動情報の多様な組み合わせによるサポートベクタマシンの分類器での頚髄症患者と健常者の分類精度を検証することで、頚髄症スクリーニングに寄与する手指運動情報を調査した。本調査の最大の分類性能となる場合で精度は78.2%、感度は84.0%、特異度は60.7%、AUCは0.85となった。また手指運動情報と頚髄症症状の重症度の関係性を示す2つの指標について、それぞれ重回帰分析を行った。その結果、推定スコアとJOAスコアの間のSpearmanの順位相関係数は0.44、推定スコアとMU-JOAスコアの間の相関係数は0.51であった。

Objective.
To develop a binary classification model for cervical myelopathy (CM) screening based on a machine learning algorithm using Leap Motion (Leap Motion, San Francisco, CA, USA), a novel non-contact sensor device.

Summary of Background Data.
Progress of CM symptoms are gradual and cannot be easily identified by the patients themselves. Therefore, screening methods should be developed for patients of CM before deterioration of myelopathy. Although some studies have been conducted to objectively evaluate hand movements specific to myelopathy using cameras or wearable sensors, their methods are unsuitable for simple screening outside hospitals because of the difficulty in obtaining and installing their equipment and the long examination time.

Methods.
In total, 50 and 28 participants in the CM and control groups were recruited, respectively. The diagnosis of CM was made by spine surgeons. We developed a desktop system using Leap Motion that recorded 35 parameters of fingertip movements while participants gripped and released their fingers as rapidly as possible. A support vector machine was used to develop the binary classification model, and a multiple linear regression analysis was performed to create regression models to estimate the total Japanese Orthopaedic Association (JOA) score and the JOA score of the motor function of the upper extremity (MU-JOA score).

Results.
The binary classification model indexes were as follows: sensitivity, 84.0%; specificity, 60.7%; accuracy, 75.6%; area under the curve, 0.85. The Spearman's rank correlation coefficient between the estimated score and the total JOA score was 0.44 and that between the estimated score and the MU-JOA score was 0.51.

Conclusion.
Our binary classification model using a machine learning algorithm and Leap Motion could classify CM with high sensitivity and would be useful for CM screening in daily life before consulting doctors and telemedicine.