Journal article
Artificial intelligence in medical imaging: A radiomic guide to precision phenotyping of cardiovascular disease
- Abstract:
- Rapid technological advances in non-invasive imaging, coupled with the availability of large data sets and the expansion of computational models and power, have revolutionized the role of imaging in medicine. Non-invasive imaging is the pillar of modern cardiovascular diagnostics, with modalities such as cardiac computed tomography (CT) now recognized as first-line options for cardiovascular risk stratification and the assessment of stable or even unstable patients. To date, cardiovascular imaging has lagged behind other fields, such as oncology, in the clinical translational of artificial intelligence (AI)-based approaches. We hereby review the current status of AI in non-invasive cardiovascular imaging, using cardiac CT as a running example of how novel machine learning (ML)-based radiomic approaches can improve clinical care. The integration of ML, deep learning, and radiomic methods has revealed direct links between tissue imaging phenotyping and tissue biology, with important clinical implications. More specifically, we discuss the current evidence, strengths, limitations, and future directions for AI in cardiac imaging and CT, as well as lessons that can be learned from other areas. Finally, we propose a scientific framework in order to ensure the clinical and scientific validity of future studies in this novel, yet highly promising field. Still in its infancy, AI-based cardiovascular imaging has a lot to offer to both the patients and their doctors as it catalyzes the transition towards a more precise phenotyping of cardiovascular disease.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Accepted manuscript, 3.4MB, Terms of use)
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- Publisher copy:
- 10.1093/cvr/cvaa021
Authors
- Publisher:
- Oxford University Press
- Journal:
- Cardiovascular Research More from this journal
- Volume:
- 116
- Issue:
- 13
- Pages:
- 2040–2054
- Publication date:
- 2020-02-24
- Acceptance date:
- 2020-01-23
- DOI:
- EISSN:
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1755-3245
- ISSN:
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0008-6363
- Language:
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English
- Keywords:
- Pubs id:
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1086955
- Local pid:
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pubs:1086955
- Deposit date:
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2020-02-11
Terms of use
- Copyright holder:
- Oikonomou et al.
- Copyright date:
- 2020
- Rights statement:
- © The Authors 2020.
- Notes:
- This is the accepted manuscript version of the article. The final version is available online from Oxford University Press at https://doi.org/10.1093/cvr/cvaa021
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