Hu Boyi, Kim Chong, Ning Xiaopeng, Xu Xu
a Department of Environmental Health , Harvard T.H. Chan School of Public Health , Boston , MA , USA.
b Department of Neurosurgery - Pain Division, School of Medicine , West Virginia University , Morgantown , WV , USA.
Ergonomics. 2018 Oct;61(10):1374-1381. doi: 10.1080/00140139.2018.1481230. Epub 2018 Jul 3.
Low back pain (LBP) remains one of the most prevalent musculoskeletal disorders, while algorithms that able to recognise LBP patients from healthy population using balance performance data are rarely seen. In this study, human balance and body sway performance during standing trials were utilised to recognise chronic LBP populations using deep neural networks. To be specific, 44 chronic LBP and healthy individuals performed static standing tasks, while their spine kinematics and centre of pressure were recorded. A deep learning network with long short-term memory units was used for training, prediction and implementation. The performance of the model was evaluated by: (a) overall accuracy, (b) precision, (c) recall, (d) F1 measure, (e) receiver-operating characteristic and (f) area under the curve. Results indicated that deep neural networks could recognise LBP populations with precision up to 97.2% and recall up to 97.2%. Meanwhile, the results showed that the model with the C7 sensor output performed the best. Practitioner summary: Low back pain (LBP) remains the most common musculoskeletal disorder. In this study, we investigated the feasibility of applying artificial intelligent deep neural network in detecting LBP population from healthy controls with their kinematics data. Results showed a deep learning network can solve the above classification problem with both promising precision and recall performance.
腰痛（LBP）仍然是最普遍的肌肉骨骼疾病之一，而利用平衡性能数据从健康人群中识别腰痛患者的算法却很少见。在本研究中，利用站立试验期间的人体平衡和身体摇摆性能，通过深度神经网络识别慢性腰痛人群。具体而言，44名慢性腰痛患者和健康个体进行了静态站立任务，同时记录了他们的脊柱运动学和压力中心。使用带有长短期记忆单元的深度学习网络进行训练、预测和实施。通过以下指标评估模型的性能：（a）总体准确率，（b）精确率，（c）召回率，（d）F1值，（e）受试者工作特征曲线，（f）曲线下面积。结果表明，深度神经网络能够精确识别腰痛人群，精确率高达97.2%，召回率高达97.2%。同时，结果表明，具有C7传感器输出的模型表现最佳。从业者总结：腰痛（LBP）仍然是最常见的肌肉骨骼疾病。在本研究中，我们研究了应用人工智能深度神经网络根据运动学数据从健康对照中检测腰痛人群的可行性。结果表明，深度学习网络能够以令人满意的精确率和召回率解决上述分类问题。
