Eckstein Jan, Moghadasi Negin, Körperich Hermann, Weise Valdés Elena, Sciacca Vanessa, Paluszkiewicz Lech, Burchert Wolfgang, Piran Misagh
Institute for Radiology, Nuclear Medicine and Molecular Imaging, Heart and Diabetes Center North-Rhine Westphalia, Ruhr-University of Bochum, 32545 Bad Oeynhausen, Germany.
Department of Engineering Systems & Environment, University of Virginia, Charlottesville, VA 22904, USA.
Diagnostics (Basel). 2022 Nov 4;12(11):2693. doi: 10.3390/diagnostics12112693.
This study challenges state-of-the-art cardiac amyloidosis (CA) diagnostics by feeding multi-chamber strain and cardiac function into supervised machine (SVM) learning algorithms.
Forty-three CA (32 males; 79 years (IQR 71; 85)), 20 patients with hypertrophic cardiomyopathy (HCM, 10 males; 63.9 years (±7.4)) and 44 healthy controls (CTRL, 23 males; 56.3 years (IQR 52.5; 62.9)) received cardiovascular magnetic resonance imaging. Left atrial, right atrial and right ventricular strain parameters and cardiac function generated a 41-feature matrix for decision tree (DT), k-nearest neighbor (KNN), SVM linear and SVM radial basis function (RBF) kernel algorithm processing. A 10-feature principal component analysis (PCA) was conducted using SVM linear and RBF.
Forty-one features resulted in diagnostic accuracies of 87.9% (AUC = 0.960) for SVM linear, 90.9% (0.996; Precision = 94%; Sensitivity = 100%; F1-Score = 97%) using RBF kernel, 84.9% (0.970) for KNN, and 78.8% (0.787) for DT. The 10-feature PCA achieved 78.9% (0.962) via linear SVM and 81.8% (0.996) via RBF SVM. Explained variance presented bi-atrial longitudinal strain and left and right atrial ejection fraction as valuable CA predictors.
SVM RBF kernel achieved competitive diagnostic accuracies under supervised conditions. Machine learning of multi-chamber cardiac strain and function may offer novel perspectives for non-contrast clinical decision-support systems in CA diagnostics.
本研究通过将多腔室应变和心脏功能输入监督机器学习（SVM）算法，对心脏淀粉样变性（CA）的现有诊断方法提出了挑战。
43例CA患者（32例男性；79岁（四分位间距71；85））、20例肥厚型心肌病（HCM）患者（10例男性；63.9岁（±7.4））和44例健康对照者（CTRL，23例男性；56.3岁（四分位间距52.5；62.9））接受了心血管磁共振成像。左心房、右心房和右心室应变参数以及心脏功能生成了一个41特征矩阵，用于决策树（DT）、k近邻（KNN）、SVM线性和SVM径向基函数（RBF）核算法处理。使用SVM线性和RBF进行了10特征主成分分析（PCA）。
41个特征在SVM线性算法下诊断准确率为87.9%（AUC = 0.960），使用RBF核算法时为90.9%（0.996；精确率 = 94%；敏感度 = 100%；F1分数 = 97%），KNN为84.9%（0.970），DT为78.8%（0.787）。10特征PCA通过线性SVM达到78.9%（0.962），通过RBF SVM达到81.8%（0.996）。可解释方差显示双心房纵向应变以及左、右心房射血分数是有价值的CA预测指标。
SVM RBF核算法在监督条件下取得了具有竞争力的诊断准确率。多腔室心脏应变和功能的机器学习可能为CA诊断中的非对比临床决策支持系统提供新的视角。
