School of Public Health, Xi'an Jiaotong University, Key Laboratory of Trace Elements and Endemic Diseases, National Health Commission of the People's Republic of China, Xi'an, Shaanxi, P.R. China.
School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, Shaanxi, P.R. China.
BMC Musculoskelet Disord. 2021 Sep 18;22(1):801. doi: 10.1186/s12891-021-04514-z.
Diagnosing Kashin-Beck disease (KBD) involves damages to multiple joints and carries variable clinical symptoms, posing great challenge to the diagnosis of KBD for clinical practitioners. However, it is still unclear which clinical features of KBD are more informative for the diagnosis of Kashin-Beck disease among adolescent.
We first manually extracted 26 possible features including clinical manifestations, and pathological changes of X-ray images from 400 KBD and 400 non-KBD adolescents. With such features, we performed four classification methods, i.e., random forest algorithms (RFA), artificial neural networks (ANNs), support vector machines (SVMs) and linear regression (LR) with four feature selection methods, i.e., RFA, minimum redundancy maximum relevance (mRMR), support vector machine recursive feature elimination (SVM-RFE) and Relief. The performance of diagnosis of KBD with respect to different classification models were evaluated by sensitivity, specificity, accuracy, and the area under the receiver operating characteristic (ROC) curve (AUC).
Our results demonstrated that the 10 out of 26 discriminative features were displayed more powerful performance, regardless of the chosen of classification models and feature selection methods. These ten discriminative features were distal end of phalanges alterations, metaphysis alterations and carpals alterations and clinical manifestations of ankle joint movement limitation, enlarged finger joints, flexion of the distal part of fingers, elbow joint movement limitation, squatting limitation, deformed finger joints, wrist joint movement limitation.
The selected ten discriminative features could provide a fast, effective diagnostic standard for KBD adolescents.
卡森-贝克病（KBD）的诊断涉及多个关节损伤，具有多变的临床症状，这给临床医生诊断 KBD 带来了极大的挑战。然而，目前仍不清楚 KBD 的哪些临床特征对青少年 KBD 的诊断更有意义。
我们首先从 400 名 KBD 和 400 名非 KBD 青少年中手动提取了 26 种可能的特征，包括临床表现和 X 光图像的病理变化。有了这些特征，我们使用了四种分类方法，即随机森林算法（RFA）、人工神经网络（ANNs）、支持向量机（SVMs）和线性回归（LR），并使用四种特征选择方法，即 RFA、最小冗余最大相关性（mRMR）、支持向量机递归特征消除（SVM-RFE）和 Relief。我们通过敏感性、特异性、准确性和接收器操作特征（ROC）曲线下的面积（AUC）评估了不同分类模型对 KBD 诊断的性能。
我们的结果表明，无论选择分类模型还是特征选择方法，26 个有区别的特征中有 10 个表现出更强大的性能。这 10 个有区别的特征是指末端指骨改变、干骺端改变和腕骨改变以及踝关节运动受限、手指关节肿大、手指远端弯曲、肘关节运动受限、蹲坐受限、手指关节变形、腕关节运动受限的临床表现。
选择的 10 个有区别的特征可为 KBD 青少年提供快速、有效的诊断标准。
