心肌淀粉样变性(CA)与左心室肥大的其他病因学病理的不同之处在于,经胸超声心动图难以根据人类视觉观察来评估纹理特征。基于超声心动图对心肌纹理的研究较少。因此,提出了一种基于超声图像纹理特征的自适应机器学习方法来识别CA。在这项回顾性研究中,共289例(心肌淀粉样变性50例;肥厚型心肌病:70例;尿毒症心肌病:92例;高血压性心脏病:77例)。我们提取了这些患者的心肌超声影像学特征,并筛选了这些特征,和四种随机森林(RF)模型,支持向量机(SVM),建立Logistic回归(LR)和梯度决策提升树(GBDT)以区分心肌淀粉样变性与其他疾病。最后,评价模型的诊断效率,并与传统超声诊断方法进行比较。在总人口中,我们建立的四种机器学习模型可以有效区分CA和非CA疾病,AUC(RF0.77,SVM0.81,LR0.81,GBDT0.71)。LR模型具有最佳的召回诊断效率,F1分数,敏感性和特异性分别为0.21、0.34、0.21和1.0。略优于传统的超声诊断模子。在进一步的亚组分析中,将心肌淀粉样变性组与肥厚型心肌病患者进行逐一比较,尿毒症心肌病,高血压心脏病组,并采用相同的方法进行特征提取和数据建模。模型的诊断效率进一步提高。值得注意的是,在确定CA组和HHD组时,AUC值达到0.92以上,准确度达到0.87以上,灵敏度等于或大于0.81,特异性0.91,F1评分高于0.84。这种基于超声心动图与机器学习相结合的新方法可能具有用于CA诊断的潜力。
Myocardial amyloidosis (CA) differs from other etiological pathologies of left ventricular hypertrophy in that transthoracic echocardiography is challenging to assess the texture features based on human visual observation. There are few studies on myocardial texture based on echocardiography. Therefore, this paper proposes an adaptive machine learning method based on ultrasonic image texture features to identify CA. In this retrospective study, a total of 289 participants (50 cases of myocardial amyloidosis; Hypertrophic cardiomyopathy: 70 cases; Uremic cardiomyopathy: 92 cases; Hypertensive heart disease: 77 cases). We extracted the myocardial ultrasonic imaging features of these patients and screened the features, and four models of random forest (RF), support vector machine (SVM), logistic regression (LR) and gradient decision-making lifting tree (GBDT) were established to distinguish myocardial amyloidosis from other diseases. Finally, the diagnostic efficiency of the model was evaluated and compared with the traditional ultrasonic diagnostic methods. In the overall population, the four machine learning models we established could effectively distinguish CA from nonCA diseases, AUC (RF 0.77, SVM 0.81, LR 0.81, GBDT 0.71). The LR model had the best diagnostic efficiency with recall, F1-score, sensitivity and specificity of 0.21, 0.34, 0.21 and 1.0, respectively. Slightly better than the traditional ultrasonic diagnosis model. In further subgroup analysis, the myocardial amyloidosis group was compared one-by-one with the patients with hypertrophic cardiomyopathy, uremic cardiomyopathy, and hypertensive heart disease groups, and the same method was used for feature extraction and data modeling. The diagnostic efficiency of the model was further improved. Notably, in identifying of the CA group and HHD group, AUC values reached more than 0.92, accuracy reached more than 0.87, sensitivity equal to or greater than 0.81, specificity 0.91, and F1 score higher than 0.84. This novel method based on echocardiography combined with machine learning may have the potential to be used in the diagnosis of CA.