A novel hybrid machine learning model for auxiliary diagnosing myocardial ischemia

作者：Jing Wang, Jing Xu, Jingsong Mao, Suzhong Fu, Haowei Gu, Naiming Wu, Guoqing Su, Zhiping Lin, Kaiyue Zhang, Yuetong Lin, Zhao Yang, Gang Liu, Hengyu Zhao, Qingliang Zhao · 发表于：Frontiers in Cardiovascular Medicine · 年份：2024 · DOI：10.3389/fcvm.2024.1327912 · 被引用次数：4 · 研究领域：Cardiovascular Disease and Adiposity、Radiomics and Machine Learning in Medical Imaging、Cardiac Imaging and Diagnostics

Introduction: Accurate identification of the myocardial texture features of fat around the coronary artery on coronary computed tomography angiography (CCTA) images are crucial to improve clinical diagnostic efficiency of myocardial ischemia (MI). However, current coronary CT examination is difficult to recognize and segment the MI characteristics accurately during earlier period of inflammation. Materials and methods: We proposed a random forest model to automatically segment myocardium and extract peripheral fat features. This hybrid machine learning (HML) model is integrated by CCTA images and clinical data. A total of 1,316 radiomics features were extracted from CCTA images. To further obtain the features that contribute the most to the diagnostic model, dimensionality reduction was applied to filter features to three: LNS, GFE, and WLGM. Moreover, statistical hypothesis tests were applied to improve the ability of discriminating and screening clinical features between the ischemic and non-ischemic groups. Results: By comparing the accuracy, recall, specificity and AUC of the three models, it can be found that HML had the best performance, with the value of 0.848, 0.762, 0.704 and 0.729. Conclusion: In sum, this study demonstrates that ML-based radiomics model showed good predictive value in MI, and offer an enhanced tool for predicting prognosis with greater accuracy.