Deep Learning in Cardiology
Deep Learning in Cardiology
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DOI：
10.1109/rbme.2018.2885714
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发表时间：
2019-01-01
影响因子：
17.6
通讯作者：
Koutsouris, Dimitrios
中科院分区：
文献类型：
--
作者：
Bizopoulos, Paschalis;Koutsouris, Dimitrios
医疗领域正在创建大量数据，医生无法有效解读和使用。此外，基于规则的专家系统在解决复杂的医疗任务或使用大数据创建见解方面效率低下。深度学习已成为诊断、预测和干预等广泛医学问题中更准确、更有效的技术。深度学习是一种表示学习方法，由非线性转换数据的层组成，从而揭示层次关系和结构。在这篇综述中，我们调查了使用结构化数据以及心脏病学信号和成像模式的深度学习应用论文。我们讨论了在心脏病学中应用深度学习的优点和局限性，这些优点和局限性也适用于一般医学，同时提出了某些最适合临床使用的方向。
The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction, and intervention. Deep learning is a representation learning method that consists of layers that transform data nonlinearly, thus, revealing hierarchical relationships and structures. In this review, we survey deep learning application papers that use structured data, and signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.