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

Authors
 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. Bax  ;  Yuanfang Guan  ;  James K. Min 
Citation
 EUROPEAN HEART JOURNAL, Vol.40(24) : 1975-1986, 2019 
Journal Title
 EUROPEAN HEART JOURNAL 
ISSN
 0195-668X 
Issue Date
2019
Keywords
Cardiovascular disease ; Coronary computed tomography angiography ; Echocardiography ;  Machine learning
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 imagingmodalities 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.
Full Text
https://academic.oup.com/eurheartj/article/40/24/1975/5060564
DOI
10.1093/eurheartj/ehy404
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
Yonsei Authors
Chang, Hyuk-Jae(장혁재) ORCID logo https://orcid.org/0000-0002-6139-7545
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/170388
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