Fibromyalgia (FM) diagnosis remains a challenge for clinicians due to a lack of objective diagnostic tools. One proposed solution is the use of quantitative ultrasound (US) techniques, such as image texture analysis, which has demonstrated discriminatory capabilities with other chronic pain conditions. From this, we propose the use of image texture variables to construct and compare two machine learning models (support vector machine [SVM] and logistic regression) for differentiating between the trapezius muscle in healthy and FM patients. US videos of the right and left trapezius muscle were acquired from healthy (n = 51) participants and those with FM (n = 57). The videos were converted into 64,800 skeletal muscle regions of interest (ROIs) using MATLAB. The ROIs were filtered by an algorithm using the complex wavelet structural similarity index (CW-SSIM), which removed ROIs that were similar. Thirty-one texture variables were extracted from the ROIs, which were then used in nested cross-validation to construct SVM and elastic net regularized logistic regression models. The generalized performance accuracy of both models was estimated and confirmed with a final validation on a holdout test set. The predicted generalized performance accuracy of the SVM and logistic regression models was computed to be 83.9 ± 2.6% and 65.8 ± 1.7%, respectively. The models achieved accuracies of 84.1%, and 66.0% on the final holdout test set, validating performance estimates. Although both machine learning models differentiate between healthy trapezius muscle and that of patients with FM, only the SVM model demonstrated clinically relevant performance levels.

中文翻译：

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使用 B 型超声图像对纤维肌痛进行分类的机器学习诊断模型

由于缺乏客观的诊断工具，纤维肌痛 (FM) 的诊断仍然是临床医生面临的挑战。一种提议的解决方案是使用定量超声 (US) 技术，例如图像纹理分析，该技术已证明具有与其他慢性疼痛状况的区分能力。由此，我们建议使用图像纹理变量来构建和比较两种机器学习模型（支持向量机 [SVM] 和逻辑回归），以区分健康和 FM 患者的斜方肌。左右斜方肌的美国视频是从健康 (n = 51) 参与者和 FM (n = 57) 参与者那里获得的。使用 MATLAB 将视频转换为 64,800 个感兴趣的骨骼肌区域 (ROI)。ROI 由使用复杂小波结构相似性指数 (CW-SSIM) 的算法过滤，该算法去除了相似的 ROI。从 ROI 中提取了 31 个纹理变量，然后将其用于嵌套交叉验证以构建 SVM 和弹性网络正则化逻辑回归模型。两种模型的广义性能准确度都通过对保持测试集的最终验证进行了估计和确认。SVM 和逻辑回归模型的预测广义性能准确度分别计算为 83.9 ± 2.6% 和 65.8 ± 1.7%。这些模型在最终保持测试集上实现了 84.1% 和 66.0% 的准确率，验证了性能估计。尽管两种机器学习模型都区分了健康的斜方肌和 FM 患者的肌肉，