Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging
Open accessAbstract
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.
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AI has been applied in various domains of healthcare including cancer research, cardiology, diabetes, mental health, identification of prognosis, Alzheimer's disease, identification of difference in clinical groups, cardiovascular disease, and stroke-related studies.
“AI has been put in application in various domains of healthcare [83–87] which includes cancer research [88], cardiology [89], diabetes [90], mental health [91], identification of prognosis [92], identification of Alzheimer's disease [93], identification of difference in the clinical groups [94], identification of cardiovascular disease [39], stroke-related studies [95], etc.”
From Applications of Artificial Intelligence and Big Data Analytics in m-Health: A Healthcare System Perspective (2020) · cites this paper as [86]
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- Authors
- 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., Bax, J. J., Guan, Y., & Min, J. K.
- Affiliations
- Department of Radiology, NewYork-Presbyterian Hospital and Weill Cornell Medicine, New York, NY, USA
- NewYork–Presbyterian Hospital (US)
- Cedars-Sinai Medical Center (US)
- University of British Columbia (CA)
- Stanford Medicine (US)
- Centro Cardiologico Monzino (IT)
- Medical University of South Carolina (US)
- Yonsei University (KR)
- Icahn School of Medicine at Mount Sinai (US)
- Leiden University Medical Center (NL)
- University of Michigan (US)
- ORCID
- Venue
- European Heart Journal
- Publisher
- Oxford University Press (OUP)
- Published
- 2018-07-27
- Volume
- 40
- Issue
- 24
- Pages
- 1975-1986
- ISSN
- 0195-668X (ISSN-L)
- 1522-9645
- OpenAlex
- W2883464116
- Cited by
- 534
- References
- 5
- Citations by year
- 2026: 40 · 2025: 61 · 2024: 70 · 2023: 90 · 2022: 74 · 2021: 82 · 2020: 68 · 2019: 44 · 2018: 5
- FWCI
- 1.09
- Type
- journal article
- Language
- en
- Open access
- Licence
- Locations
- European Heart Journal (publishedVersion)
- PubMed (publishedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Leiden Repository (Leiden University) (submittedVersion)
- YUHSpace (Yonsei University Medical Library) (submittedVersion, cc-by-nc-nd)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Cardiac Imaging and Diagnostics
- Keywords
- machine learning, artificial intelligence, cardiovascular disease, cardiac imaging, echocardiography, electrocardiogram, coronary artery calcium scoring, coronary CT angiography, predictive modeling, clinical application
- MeSH terms
- Machine Learning, Positron Emission Tomography Computed Tomography, Computed Tomography Angiography, Algorithms, Artificial Intelligence, Calcium, Cardiovascular Diseases, Coronary Vessels, Echocardiography, Electrocardiography, Heart Failure, Humans, Sensitivity and Specificity, Tomography, Emission-Computed, Single-Photon, Neural Networks, Computer, Cardiac Imaging Techniques
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