Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging. (27th July 2018)

• Record Type: Journal Article
• Authors: Al'Aref, Subhi J; Anchouche, Khalil; Singh, Gurpreet; Slomka, Piotr J; Kolli, Kranthi K; Kumar, Amit; Pandey, Mohit; Maliakal, Gabriel; van Rosendael, Alexander R; Beecy, Ashley N; Berman, Daniel S; Leipsic, Jonathan; Nieman, Koen; Andreini, Daniele; Pontone, Gianluca; Schoepf, U Joseph; Shaw, Leslee J; Chang, Hyuk-Jae; Narula, Jagat; Bax, Jeroen J; Guan, Yuanfang; Min, James K
• Is Part Of: European heart journal. Volume 40:Number 24(2019)
• Page Start: 1975
• Page End: 1986
• Publication Date: 2018-07-27
• Subjects: Machine learning -- Cardiovascular disease -- Coronary computed tomography angiography -- Echocardiography; Cardiology -- Periodicals; Heart -- Diseases -- Periodicals; 616.12005
• DOI: 10.1093/eurheartj/ehy404
• Languages: English
• ISSNs: 0195-668X

Abstract

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.