Binary classification of non-specific low back pain condition based on the combination of B-mode ultrasound and shear wave elastography at multiple sites - Yu, Xiaocheng; Xu, Xiaohua; Huang, Qinghua; Zhu, Guowen; Xu, Faying; Liu, Zhenhua; Su, Lin; Zheng, Haiping; Zhou, Chen; Chen, Qiuming; Gao, Fen; Lin, Mengting; et al. (Frontiers in Physiology, 2023)
Low back pain (LBP) is a prevalent and complex condition that poses significant medical, social, and economic burdens worldwide. The accurate and timely assessment and diagnosis of LBP, particularly non-specific LBP (NSLBP), are crucial to developing effective interventions and treatments for LBP patients. In this study, we aimed to investigate the potential of combining B-mode ultrasound image features with shear wave elastography (SWE) features to improve the classification of NSLBP patients. We recruited 52 subjects with NSLBP from the University of Hong Kong-Shenzhen Hospital and collected B-mode ultrasound images and SWE data from multiple sites. We then extracted and selected features from the data and employed a support vector machines (SVM) model to classify NSLBP patients with the Visual Analogue Scale (VAS) as the ground truth. We evaluated the performance of the SVM model using five-fold cross-validation and calculated accuracy, precision, and sensitivity. We obtained an optimal feature set of 48 features, among which the SWE elasticity feature had the most significant contribution to the classification task. The SVM model achieved an accuracy, precision, and sensitivity of 0.85, 0.89, and 0.86, respectively, which were higher than the previously reported values of MRI. Our study demonstrates that combining B-mode ultrasound image features with SWE features and employing an SVM model can improve the automatic classification of NSLBP patients. Our findings also suggest that the SWE elasticity feature is a crucial factor in classifying NSLBP patients, and the proposed method can identify the important site and position of the muscle in the NSLBP classification task. These results have important implications for improving the accuracy and efficiency of diagnosing and treating NSLBP patients..