Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging
Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging
复制标题
DOI：
10.1093/eurheartj/ehy404
复制
发表时间：
2019-06-21
影响因子：
39.3
通讯作者：
Min, James K.
中科院分区：
文献类型：
--
作者：
Al'Aref, Subhi J.;Anchouche, Khalil;Min, James K.
人工智能（AI）已经改变了人类生活的关键方面。机器学习（ML）是人工智能的一个子集，其中机器通过从大型数据库中提取模式来自主获取信息，已越来越多地用于医学界，特别是心血管疾病领域。在这篇综述中，我们简要概述了用于构建推理和预测数据驱动模型的ML方法。我们强调了ML应用的几个领域，如超声心动图，心电图，以及最近开发的非侵入性成像方式，如冠状动脉钙评分和冠状动脉计算机断层扫描血管造影。最后，我们回顾了ML算法在心血管疾病领域的当代应用相关的局限性。
Artificial intelligence (AI) has transformed key aspects of human life. Machine learning (ML), which is a subset of AI wherein machines autonomously acquire information by extracting patterns from large databases, has been increasingly used within the medical community, and specifically within the domain of cardiovascular diseases. In this review, we present a brief overview of ML methodologies that are used for the construction of inferential and predictive data-driven models. We highlight several domains of ML application such as echocardiography, electrocardiography, and recently developed non-invasive imaging modalities such as coronary artery calcium scoring and coronary computed tomography angiography. We conclude by reviewing the limitations associated with contemporary application of ML algorithms within the cardiovascular disease field.