Clinical Implications of Machine Learning, Artificial Intelligence, and Radiomics in Cardiac Imaging
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Imaging (Q Truong, Section Editor)
Clinical Implications of Machine Learning, Artificial Intelligence, and Radiomics in Cardiac Imaging Brian Yum, MD Andrew Adelsheimer, MD Romina Tafreshi, BA Raffi Hagopian, MD Jiwon Kim, MD* Address * Greenberg Cardiology Division, Department of Medicine, Weill Cornell Medicine, 525 East 68th Street, New York, NY, 10021, USA Email: [email protected]
Published online: 10 October 2020 * Springer Science+Business Media, LLC, part of Springer Nature 2020
This article is part of the Topical Collection on Imaging Keywords Machine learning I Artificial intelligence I Radiomics I Cardiac imaging
Abstract Purpose of review Rapid advancements in technology and electronic medical record systems have given rise to massive amounts of cardiac imaging data with the potential to alter medical practices. The rise of machine learning (ML) and radiomics – the concept that images contain invaluable data regarding disease processes beyond what the eyes can see – promises increased precision and accuracy to the current standard of care. Recent advancements in major cardiac imaging modalities, such as echocardiography, cardiac CT and cardiac MRI, have uncovered promising diagnostic and prognostic information through the application of ML. Recent findings In echocardiography, ML has been successfully applied to identify views, make right- and left-sided heart measurements, and detect certain diseases like hypertrophic cardiomyopathy. Application of ML in cardiac CT has seen success in quantifying coronary plaque burden, identifying significant coronary stenosis, and predicting mortality in coronary artery disease (CAD). In cardiac MRI, efforts have been made to automatize segmentation for chamber measurements and detecting fibrosis. For nuclear imaging, ML has been applied to not only make measurements like the left ventricular ejection fraction, but also to identify perfusion abnormalities and predict obstructive CAD. Summary While there are many milestones still to be reached before ML can be widely
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integrated to current clinical practices, there is optimism for ML to advance the field of cardiovascular imaging through enhanced image analysis and improved efficiency.
Introduction/defining terms In the age of cloud computing, there have been advancements in capabilities to store and share large amounts of information. Collecting large amounts of information on numerous patients is what ultimately make up “big data” [1]. The strength of big data is, by definition, quantity. However, without standardization or curation of individual data points, big data becomes clusters of information that cannot be properly analyzed. Artificial intelligence (AI) and machine learning (ML) allow clinicians to take full advantage of big data for increased accuracy and precision in medical decision-making. AI refers to designing algorithms that behave in ways that are characteristic of human decisionmaking. The means by which AI can learn is through
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