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 the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, 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.

翻译：医学领域正在产生大量数据，而医生无法有效解读和利用这些数据。此外，基于规则的专家系统在解决复杂医疗任务或利用大数据获取洞察方面效率低下。深度学习已成为在诊断、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，由多个非线性变换数据的层级组成，从而揭示层次化关系和结构。在本综述中，我们调查了使用结构化数据、信号和影像模态来自心脏病学的深度学习应用论文。我们讨论了将深度学习应用于心脏病学（也普遍适用于医学领域）的优势与局限，同时提出了最适用于临床实践的若干方向。