Artificial Intelligence Will Transform Cardiac Imaging-Opportunities and Challenges
Petersen, Steffen E; Abdulkareem, Musa; Leiner, Tim
(2019) Frontiers in cardiovascular medicine, volume 6
(Article)
Abstract
Artificial intelligence (AI) using machine learning techniques will change healthcare as we know it. While healthcare AI applications are currently trailing behind popular AI applications, such as personalized web-based advertising, the pace of research and deployment is picking up and about to become disruptive. Overcoming challenges such as patient and
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public support, transparency over the legal basis for healthcare data use, privacy preservation, technical challenges related to accessing large-scale data from healthcare systems not designed for Big Data analysis, and deployment of AI in routine clinical practice will be crucial. Cardiac imaging and imaging of other body parts is likely to be at the frontier for the development of applications as pattern recognition and machine learning are a significant strength of AI with practical links to image processing. Many opportunities in cardiac imaging exist where AI will impact patients, medical staff, hospitals, commissioners and thus, the entire healthcare system. This perspective article will outline our vision for AI in cardiac imaging with examples of potential applications, challenges and some lessons learnt in recent years.
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Keywords: artificial intelligence, cardiac magnetic resonance (CMR), deep learning, cardiac imaging, echocardiography, cardiac CT angiogram, cardiac nuclear imaging, echocardiagraphy, Cardiology and Cardiovascular Medicine, Review, Journal Article
ISSN: 2297-055X
Publisher: Frontiers Media S. A.
Note: Funding Information: TL acknowledges research support from Applied and Engineering Sciences of The Netherlands Organization for Scientific Research, the Netherlands Heart Foundation and discloses institutional grants received from Philips Healthcare and Pie Medical B.V. He is co-founder of Quantib-U. Funding Information: SP acts as a paid consultant to Circle Cardiovascular Imaging Inc., Calgary, Canada and Servier. SP acknowledges support from the National Institute for Health Research (NIHR) Cardiovascular Biomedical Research Center at Barts, from the SmartHeart EPSRC programme grant (www.nihr.ac.uk; EP/P001009/1) and the London Medical Imaging and AI Center for Value-Based Healthcare. This new center is one of the UK Centers supported by a £50 m investment from the Data to Early Diagnosis and Precision Medicine strand of the government’s Industrial Strategy Challenge Fund, managed, and delivered by UK Research and Innovation (UKRI). SP and MA acknowledge support from the CAP-AI programme, London’s first AI enabling programme focused on stimulating growth in the capital’s AI Sector. CAP-AI is led by Capital Enterprise in partnership with Barts Health NHS Trust and Digital Catapult and was funded by the European Regional Development Fund and Barts Charity. SP acknowledges funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 825903. Funding Information: SP acts as a paid consultant to Circle Cardiovascular Imaging Inc., Calgary, Canada and Servier. SP acknowledges support from the National Institute for Health Research (NIHR) Cardiovascular Biomedical Research Center at Barts, from the SmartHeart EPSRC programme grant (www.nihr.ac.uk ; EP/P001009/1) and the London Medical Imaging and AI Center for Value-Based Healthcare. This new center is one of the UK Centers supported by a £50 m investment from the Data to Early Diagnosis and Precision Medicine strand of the government's Industrial Strategy Challenge Fund, managed, and delivered by UK Research and Innovation (UKRI). SP and MA acknowledge support from the CAP-AI programme, London's first AI enabling programme focused on stimulating growth in the capital's AI Sector. CAP-AI is led by Capital Enterprise in partnership with Barts Health NHS Trust and Digital Catapult and was funded by the European Regional Development Fund and Barts Charity. SP acknowledges funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 825903. TL acknowledges research support from Applied and Engineering Sciences of The Netherlands Organization for Scientific Research, the Netherlands Heart Foundation and discloses institutional grants received from Philips Healthcare and Pie Medical B.V. He is co-founder of Quantib-U. The funders provided support in the form of salaries but did not have any additional role in the preparation of the manuscript. The authors would like to thank Wendy Lo and Troy Tye from Circle Cardiovascular Imaging Inc., Calgary, Canada, for turning a table with our ideas into the infographic in Figure 1. A big thanks also to Nay Aung, Kenneth Fung, and Alireza Sojoudi for their critical review of this article. Publisher Copyright: © Copyright © 2019 Petersen, Abdulkareem and Leiner.
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