Machine learning-enabled screening for aortic stenosis with handheld ultrasound.

Journal: European heart journal. Imaging methods and practice
Published Date: Jan 1, 2025

Abstract

AIMS: Neural network classifiers can detect aortic stenosis (AS) using limited cardiac ultrasound images. While networks perform very well using cart-based imaging, they have never been tested or fine-tuned for use with focused cardiac ultrasound (FoCUS) acquisitions obtained on handheld ultrasound devices.

Authors

  • Samuel Karmiy
    Department of Medicine, Tufts Medical Center, Boston, Massachusetts.
  • Zhe Huang
  • Divya Velury
    Department of Medicine, Tufts Medical Center, Boston, MA, USA.
  • Eileen Mai
    CardioVascular Center, Tufts Medical Center, Boston, Massachusetts.
  • Jing Li
    Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China.
  • Monica M Dehn
    CardioVascular Center, Tufts Medical Center, Boston, Massachusetts.
  • Dikran R Balian
    Tufts University School of Medicine, Boston, MA, USA.
  • Davinder Ramsingh
    Department of Anesthesiology, Loma Linda University Medical Center, 11234 Anderson St, Loma Linda, CA, 92354, USA.
  • John Martin
    Butterfly Network, Inc., Guilford, CT 06437.
  • Jacob Kantrowitz
    Department of Medicine, Tufts Medical Center, Boston, MA, USA.
  • Ayan R Patel
    CardioVascular Center, Tufts Medical Center, Boston, Massachusetts.
  • Michael C Hughes
    Department of Computer Science, Tufts University, 161 College Ave, Medford, MA, 02155, USA.
  • Benjamin S Wessler
    CardioVascular Center, Tufts Medical Center, Boston, Massachusetts. Electronic address: bwessler@tuftsmedicalcenter.org.

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