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The role of artificial intelligence in cardiovascular magnetic resonance imaging.
Aromiwura, Afolasayo A; Cavalcante, João L; Kwong, Raymond Y; Ghazipour, Aryan; Amini, Amir; Bax, Jeroen; Raman, Subha; Pontone, Gianluca; Kalra, Dinesh K.
Afiliación
  • Aromiwura AA; Department of Medicine, University of Louisville, Louisville, KY, USA.
  • Cavalcante JL; Minneapolis Heart Institute, Minneapolis, MN, USA.
  • Kwong RY; Division of Cardiovascular Medicine, Brigham and Women's Hospital, Boston, MA, USA.
  • Ghazipour A; Medical Imaging Laboratory, Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA.
  • Amini A; Medical Imaging Laboratory, Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA.
  • Bax J; Department of Cardiology, Leiden University, Leiden, the Netherlands.
  • Raman S; Division of Cardiology, Indiana University School of Medicine, Indianapolis, IN, USA.
  • Pontone G; Department of Cardiology and Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, University of Milan, Milan, Italy.
  • Kalra DK; Division of Cardiology, Department of Medicine, University of Louisville, Louisville, KY, USA; Center for Artificial Intelligence in Radiological Sciences (CAIRS), Department of Radiology, University of Louisville, Louisville, KY, USA. Electronic address: dinesh.kalra@louisville.edu.
Prog Cardiovasc Dis ; 2024 Jun 24.
Article en En | MEDLINE | ID: mdl-38925255
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.
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Texto completo: 1 Base de datos: MEDLINE Idioma: En Revista: Prog Cardiovasc Dis Año: 2024 Tipo del documento: Article

Texto completo: 1 Base de datos: MEDLINE Idioma: En Revista: Prog Cardiovasc Dis Año: 2024 Tipo del documento: Article