CeREF Technique, Chaussée de Binche 159, 7000 Mons, Belgium.
Traitement Formation Thérapie Manuelle (TFTM), Private Physiotherapy/Manual Therapy Center, Avenue des Cerisiers 211A, 1200 Brussels, Belgium.
Sensors (Basel). 2022 Apr 6;22(7):2805. doi: 10.3390/s22072805.
Understanding neck pain is an important societal issue. Kinematic data from sensors may help to gain insight into the pathophysiological mechanisms associated with neck pain through a quantitative sensorimotor assessment of one patient. The objective of this study was to evaluate the potential usefulness of artificial intelligence with several machine learning (ML) algorithms in assessing neck sensorimotor performance. Angular velocity and acceleration measured by an inertial sensor placed on the forehead during the DidRen laser test in thirty-eight acute and subacute non-specific neck pain (ANSP) patients were compared to forty-two healthy control participants (HCP). Seven supervised ML algorithms were chosen for the predictions. The most informative kinematic features were computed using Sequential Feature Selection methods. The best performing algorithm is the Linear Support Vector Machine with an accuracy of 82% and Area Under Curve of 84%. The best discriminative kinematic feature between ANSP patients and HCP is the first quartile of head pitch angular velocity. This study has shown that supervised ML algorithms could be used to classify ANSP patients and identify discriminatory kinematic features potentially useful for clinicians in the assessment and monitoring of the neck sensorimotor performance in ANSP patients.
理解颈部疼痛是一个重要的社会问题。通过对一位患者进行定量感觉运动评估，传感器的运动学数据可能有助于深入了解与颈部疼痛相关的病理生理机制。本研究的目的是评估人工智能与几种机器学习（ML）算法在评估颈部感觉运动性能方面的潜在有用性。在 38 名急性和亚急性非特异性颈部疼痛（ANSP）患者和 42 名健康对照参与者（HCP）进行迪德伦激光测试期间，放置在前额上的惯性传感器测量的角速度和加速度。选择了七种有监督的 ML 算法进行预测。使用顺序特征选择方法计算了最具信息量的运动学特征。表现最好的算法是线性支持向量机，准确率为 82%，曲线下面积为 84%。区分 ANSP 患者和 HCP 的最佳运动学特征是头部俯仰角速度的第一四分位数。本研究表明，监督 ML 算法可用于对 ANSP 患者进行分类，并确定潜在有助于临床医生评估和监测 ANSP 患者颈部感觉运动性能的有区别的运动学特征。
