背景:由于其非特异性的临床和表型表现而受阻,心脏结节病(CS)仍然是一个具有挑战性的诊断。
目的:利用心脏磁共振成像(CMR),我们获得了基于支持向量机学习(SVM)的CS诊断方法的多腔体积和应变特征跟踪。
方法:45个CMR阴性(CMR(-),56.5(53.0;63.0)年),CMR阳性(CMR(+),64.0(57.8;67.0)年)结节病患者和44名对照(CTRL,56.5(53.0;63.0)年)接受了CMR检查。使用逻辑回归的分类器处理心脏参数,KNN(K-最近邻),DT(决策树),RF(随机森林),SVM,GBoost,XGBoost,投票和功能选择。
结果:在CTRL与vs.的三聚类分析中CMR(+)vs.CMR(-),RF和Voting分类器产生最高的预测率(81.82%)。CTRL与CTRL的双聚类分析所有结节病(所有Sarc。)用分类器逻辑回归产生了很高的预测率,RF和SVM(96.97%),和低预测率的分析CMR(+)与CMR(-),使用特征选择和逻辑回归(89.47%)进行了扩充。
结论:多腔心功能和基于应变的监督机器学习提供了一种非对比方法,可以准确区分健康个体和结节病患者。特征选择克服了CMR(+)和CMR(-)患者之间在算法上具有挑战性的区分,产生高精度预测。研究结果表明,心脏受累的患病率高于先前的预期,这可能会影响临床疾病管理。
BACKGROUND: Hindered by its unspecific clinical and phenotypical presentation, cardiac sarcoidosis (CS) remains a challenging diagnosis.
OBJECTIVE: Utilizing cardiac magnetic resonance imaging (CMR), we acquired multi-chamber volumetrics and strain feature tracking for a support vector machine learning (SVM)-based diagnostic approach to CS.
METHODS: Forty-five CMR-negative (CMR(-), 56.5(53.0;63.0)years), eighteen CMR-positive (CMR(+), 64.0(57.8;67.0)years) sarcoidosis patients and forty-four controls (CTRL, 56.5(53.0;63.0)years)) underwent CMR examination. Cardiac parameters were processed using the classifiers of logistic regression, KNN(K-nearest-neighbor), DT (decision tree), RF (random forest), SVM, GBoost, XGBoost, Voting and feature selection.
RESULTS: In a three-cluster analysis of CTRL versus vs. CMR(+) vs. CMR(-), RF and Voting classifier yielded the highest prediction rates (81.82%). The two-cluster analysis of CTRL vs. all sarcoidosis (All Sarc.) yielded high prediction rates with the classifiers logistic regression, RF and SVM (96.97%), and low prediction rates for the analysis of CMR(+) vs. CMR(-), which were augmented using feature selection with logistic regression (89.47%).
CONCLUSIONS: Multi-chamber cardiac function and strain-based supervised machine learning provides a non-contrast approach to accurately differentiate between healthy individuals and sarcoidosis patients. Feature selection overcomes the algorithmically challenging discrimination between CMR(+) and CMR(-) patients, yielding high accuracy predictions. The study findings imply higher prevalence of cardiac involvement than previously anticipated, which may impact clinical disease management.