Cardiovascular magnetic resonance (CMR) imaging is the gold standard test for myocardial tissue characterization and chamber volumetric and functional evaluation. However, manual CMR analysis can be time-consuming and is subject to intra- and inter-observer variability. Artificial intelligence (AI) is a field that permits automated task performance through the identification of high-level and complex data relationships. In this review, we review the rapidly growing role of AI in CMR, including image acquisition, sequence prescription, artifact detection, reconstruction, segmentation, and data reporting and analysis including quantification of volumes, function, myocardial infarction (MI) and scar detection, and prediction of outcomes. We conclude with a discussion of the emerging challenges to widespread adoption and solutions that will allow for successful, broader uptake of this powerful technology.

The role of artificial intelligence in cardiovascular magnetic resonance imaging / A.A. Aromiwura, J.L. Cavalcante, R.Y. Kwong, A. Ghazipour, A. Amini, J. Bax, S. Raman, G. Pontone, D.K. Kalra. - In: PROGRESS IN CARDIOVASCULAR DISEASES. - ISSN 0033-0620. - 86:(2024), pp. 13-25. [10.1016/j.pcad.2024.06.004]

The role of artificial intelligence in cardiovascular magnetic resonance imaging

G. Pontone
Penultimo
;
2024

Abstract

Cardiovascular magnetic resonance (CMR) imaging is the gold standard test for myocardial tissue characterization and chamber volumetric and functional evaluation. However, manual CMR analysis can be time-consuming and is subject to intra- and inter-observer variability. Artificial intelligence (AI) is a field that permits automated task performance through the identification of high-level and complex data relationships. In this review, we review the rapidly growing role of AI in CMR, including image acquisition, sequence prescription, artifact detection, reconstruction, segmentation, and data reporting and analysis including quantification of volumes, function, myocardial infarction (MI) and scar detection, and prediction of outcomes. We conclude with a discussion of the emerging challenges to widespread adoption and solutions that will allow for successful, broader uptake of this powerful technology.
Artificial intelligence; Cardiology; Cardiovascular magnetic resonance; Deep learning
Settore MED/11 - Malattie dell'Apparato Cardiovascolare
Settore MEDS-07/B - Malattie dell'apparato cardiovascolare
2024
24-giu-2024
Article (author)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/1074048
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