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 journalArticle

Abstract

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
JournalEuropean Heart Journal
DOIs
Publication statusE-pub ahead of print - Jul 27 2018

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    Al'Aref, S. J., Anchouche, K., Singh, G., Slomka, P. J., Kolli, K. K., Kumar, A., Pandey, M., Maliakal, G., van Rosendael, A. R., Beecy, A. N., Berman, D. S., Leipsic, J., Nieman, K., Andreini, D., Pontone, G., Schoepf, U. J., Shaw, L. J., Chang, H-J., Narula, J., ... Min, J. K. (2018). Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging. European Heart Journal. https://doi.org/10.1093/eurheartj/ehy404