医学
部分流量储备
狭窄
内科学
心脏病学
冠状动脉钙评分
逻辑回归
冠状动脉疾病
机器学习
支持向量机
动脉
人工智能
放射科
冠状动脉造影
心肌梗塞
计算机科学
冠状动脉钙
作者
Ying Zhang,Ping Liu,Ting Lin,Peimin Lin,Run Liu,Huai‐Rong Luo,Pei Luo
标识
DOI:10.1016/j.compbiomed.2023.107130
摘要
To obtain the coronary artery calcium score (CACS) for each branch in coronary artery computed tomography angiography (CCTA) examination combined with the flow fraction reserve (FFR) of each branch in the coronary artery detected by CT and apply a machine learning model (ML) to analyse and predict the severity of coronary artery stenosis.All patients who underwent coronary computed tomography angiography (CCTA) from January 2019 to April 2022 in the HOSPITAL (T.C.M) AFFILIATED TO SOUTHWEST MEDICAL UNIVERSITY) were retrospectively screened, and their sex, age, characteristics of lipid-containing lesions, coronary calcium score (CACS) and CT-FFR values were collected. Five machine learning models, random forest (RF), k-nearest neighbour algorithm (KNN), kernel logistic regression, support vector machine (SVM) and radial basis function neural network (RBFNN), were used as predictive models to evaluate the severity of coronary stenosis.Among the five machine learning models, the SVM model achieved the best prediction performance, and the prediction accuracy of mild stenosis was up to 90%. Second, age and male sex were important influencing factors of increasing CACS and decreasing CT-FFR. Moreover, the critical CACS value of myocardial ischemia >200.70 was calculated.Through computer machine learning model analysis, we prove the importance of CACS and FFR in predicting coronary stenosis, especially the prominent vector machine model, which promotes the application of artificial intelligence computer learning methods in the field of medical analysis.
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