Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging

Subhi J. Al'Aref, Khalil Anchouche, Gurpreet Singh, Piotr J. Slomka, Kranthi K. Kolli, Amit Kumar, Mohit Pandey, Gabriel Maliakal, Alexander R. Van Rosendael, Ashley N. Beecy, Daniel S. Berman, Jonathan Leipsic, Koen Nieman, Daniele Andreini, Gianluca Pontone, U. Joseph Schoepf, Leslee J. Shaw, Hyuk Jae Chang, Jagat Narula, Jeroen J. BaxYuanfang Guan, James K. Min

Research output: Contribution to journalReview articlepeer-review

220 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1975-1986
Number of pages12
JournalEuropean heart journal
Issue number24
Publication statusPublished - 2019 Jun 21

Bibliographical note

Publisher Copyright:
©The Author(s) 2019.

All Science Journal Classification (ASJC) codes

  • Cardiology and Cardiovascular Medicine


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