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JACC Cardiovasc Imaging. 2019 Aug;12(8 Pt 1):1549-1565. doi: 10.1016/j.jcmg.2019.06.009.

State-of-the-Art Deep Learning in Cardiovascular Image Analysis.

Author information

1
Department of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands. Electronic address: geert.litjens@radboudumc.nl.
2
Department of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands.
3
Image Sciences Institute, University Medical Center Utrecht, Utrecht, the Netherlands.
4
Department of Radiology, University Medical Center Utrecht, Utrecht, the Netherlands.
5
Department of Radiology, Radboud University Medical Center, Nijmegen, the Netherlands; Department of Radiation Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands.

Abstract

Cardiovascular imaging is going to change substantially in the next decade, fueled by the deep learning revolution. For medical professionals, it is important to keep track of these developments to ensure that deep learning can have meaningful impact on clinical practice. This review aims to be a stepping stone in this process. The general concepts underlying most successful deep learning algorithms are explained, and an overview of the state-of-the-art deep learning in cardiovascular imaging is provided. This review discusses >80 papers, covering modalities ranging from cardiac magnetic resonance, computed tomography, and single-photon emission computed tomography, to intravascular optical coherence tomography and echocardiography. Many different machines learning algorithms were used throughout these papers, with the most common being convolutional neural networks. Recent algorithms such as generative adversarial models were also used. The potential implications of deep learning algorithms on clinical practice, now and in the near future, are discussed.

KEYWORDS:

artificial intelligence; cardiovascular imaging; deep learning

PMID:
31395244
DOI:
10.1016/j.jcmg.2019.06.009

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