An Automated Machine Learning-Based Quantitative Multiparametric Approach for Mitral Regurgitation Severity Grading.

Journal: JACC. Cardiovascular imaging
PMID: 39152959

Abstract

BACKGROUND: Considering the high prevalence of mitral regurgitation (MR) and the highly subjective, variable MR severity reporting, an automated tool that could screen patients for clinically significant MR (≥ moderate) would streamline the diagnostic/therapeutic pathways and ultimately improve patient outcomes.

Authors

  • Anita Sadeghpour
    MedStar Heart and Vascular Institute/Health Research Institute, Washington, DC, USA.
  • Zhubo Jiang
    Us2.ai, Singapore, Singapore.
  • Yoran M Hummel
    Us2.ai, Singapore, Singapore.
  • Matthew Frost
    Us2.ai, Singapore, Singapore.
  • Carolyn S P Lam
    Duke-NUS Medical School, 8 College Rd, Singapore 169857, Singapore.
  • Sanjiv J Shah
    Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
  • Lars H Lund
    Department of Cardiology, Heart, Vascular and Neuro Theme, Karolinska University Hospital, Stockholm, Sweden.
  • Gregg W Stone
  • Madhav Swaminathan
    Department of Anesthesiology, Duke University, Durham, North Carolina, United States of America.
  • Neil J Weissman
    MedStar Health Research Institute, Washington, DC.
  • Federico M Asch
    MedStar Health Research Institute, Washington DC (F.M.A.).

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