Applications of artificial intelligence in multimodality cardiovascular imaging: A state-of-the-art review

Bo Xu; Duygu Kocyigit; Richard Grimm; Brian P Griffin; Feixiong Cheng

doi:10.1016/j.pcad.2020.03.003

Applications of artificial intelligence in multimodality cardiovascular imaging: A state-of-the-art review

Prog Cardiovasc Dis. 2020 May-Jun;63(3):367-376. doi: 10.1016/j.pcad.2020.03.003. Epub 2020 Mar 19.

Authors

Bo Xu¹, Duygu Kocyigit², Richard Grimm², Brian P Griffin², Feixiong Cheng³

Affiliations

¹ Section of Cardiovascular Imaging, Robert and Suzanne Tomsich Department of Cardiovascular Medicine, Sydell and Arnold Miller Family Heart, Vascular and Thoracic Institute, Cleveland Clinic, OH, USA. Electronic address: xub@ccf.org.
² Section of Cardiovascular Imaging, Robert and Suzanne Tomsich Department of Cardiovascular Medicine, Sydell and Arnold Miller Family Heart, Vascular and Thoracic Institute, Cleveland Clinic, OH, USA.
³ Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA; Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44106, USA; Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, Ohio 44106, USA.

PMID: 32201286
DOI: 10.1016/j.pcad.2020.03.003

Abstract

There has been a tidal wave of recent interest in artificial intelligence (AI), machine learning and deep learning approaches in cardiovascular (CV) medicine. In the era of modern medicine, AI and electronic health records hold the promise to improve the understanding of disease conditions and bring a personalized approach to CV care. The field of CV imaging (CVI), incorporating echocardiography, cardiac computed tomography, cardiac magnetic resonance imaging and nuclear imaging, with sophisticated imaging techniques and high volumes of imaging data, is primed to be at the forefront of the revolution in precision cardiology. This review provides a contemporary overview of the CVI imaging applications of AI, including a critique of the strengths and potential limitations of deep learning approaches.

Keywords: Artificial intelligence; Cardiac computed tomography; Cardiac magnetic resonance; Deep learning; Echocardiography; Machine learning; Nuclear cardiac imaging.

Publication types

Review

MeSH terms

Cardiovascular Diseases / diagnostic imaging*
Deep Learning
Diagnosis, Computer-Assisted*
Humans
Image Interpretation, Computer-Assisted*
Machine Learning*
Multimodal Imaging*
Predictive Value of Tests
Reproducibility of Results
Support Vector Machine