Improving the efficiency and accuracy of cardiovascular magnetic resonance with artificial intelligence-review of evidence and proposition of a roadmap to clinical translation.

Journal: Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
PMID: 38909656

Abstract

BACKGROUND: Cardiovascular magnetic resonance (CMR) is an important imaging modality for the assessment of heart disease; however, limitations of CMR include long exam times and high complexity compared to other cardiac imaging modalities. Recently advancements in artificial intelligence (AI) technology have shown great potential to address many CMR limitations. While the developments are remarkable, translation of AI-based methods into real-world CMR clinical practice remains at a nascent stage and much work lies ahead to realize the full potential of AI for CMR.

Authors

  • Qiang Zhang
    Yunan Provincial Center for Disease Control and Prevention, Kunming 650022, China.
  • Anastasia Fotaki
    School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK; Royal Brompton Hospital, Guy's and St Thomas' NHS Foundation Trust, London, UK. Electronic address: anastasia.fotaki@kcl.ac.uk.
  • Sona Ghadimi
    Department of Biomedical Engineering, University of Virginia, Health System, Box 800759, Charlottesville, VA, 22908, USA.
  • Yu Wang
    Clinical and Technical Support, Philips Healthcare, Shanghai, China.
  • Mariya Doneva
    Philips Innovative Technologies, Hamburg, Germany. Electronic address: mariya.doneva@philips.com.
  • Jens Wetzl
    Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
  • Jana G Delfino
    US Food and Drug Administration, Silver Spring, Maryland.
  • Declan P O'Regan
    MRC London Institute of Medical Sciences, Imperial College London, London, United Kingdom.
  • Claudia Prieto
    School of Biomedical Engineering & Imaging Sciences, King's College, London, UK.
  • Frederick H Epstein
    Department of Biomedical Engineering, University of Virginia, Health System, Box 800759, Charlottesville, VA, 22908, USA. fhe6b@virginia.edu.

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