摘要
心脏计算机断层扫描血管造影术(CCTA)已成为评估冠状动脉疾病的重要非侵入性手段,随着其在临床的广泛应用及图像分析特征的增加,CCTA图像评估对技术及时间的要求不断提高。机器学习(ML)是人工智能的分支领域,它完全由数据驱动,通过计算机算法对大型数据集中变量的潜在关系进行识别及分析,实现对外部数据的预测。在心脏CT领域,不同ML算法的应用可提高CCTA的成像效率及质量,有助于病变评估及风险分层,同时也为心脏功能学成像提供了新的应用。该文主要对ML在心脏CT图像分析、风险模型、CT心肌灌注及CT血流储备分数中的应用研究进展进行综述。
Cardiac computed tomography angiography(CCTA)has become an important non-invasive method to evaluate coronary artery disease.With the extensive application and increased image analysis features,more demands on operational technique and efficiency are asked.Machine learning(ML)is the subarea of artificial intelligence(AI),which is completely data driven,by computer algorithm to identify and analyze the potential relationship of centralized variables in large data sets for realizing the prediction of external data.In the field of cardiac CT,the application of various ML algorithms would improve the efficiency and quality of CCTA,helping accurate lesion assessment and risk stratification.It also brings new applications in cardiac functional imaging.The applications of ML in cardiac CT have been reviewed in present paper including CT-image analysis,risk stratification,CT-myocardial perfusion and CT-fractional flow reserve.
作者
刘子暖
杨俊杰
陈韵岱
Liu Zi-Nuan;Yang Jun-Jie;Chen Yun-Dai(Department of Cardiovascular Medicine,the First Medical Center of Chinese PLA General Hospital,Beijing 100853,China;School of Medicine,Nankai University,Tianjin 300071,China)
出处
《解放军医学杂志》
CAS
CSCD
北大核心
2021年第3期286-293,共8页
Medical Journal of Chinese People's Liberation Army
基金
国家重点研发计划(2016YFC1300304)
北京市科技新星计划(Z181100006218055)
解放军总医院医疗大数据与人工智能研发项目(2019MBD035)。
关键词
人工智能
机器学习
体层摄影术
X线计算机
冠状动脉疾病
artificial intelligence
machine learning
tomography,X-ray computed
coronary artery disease
