A New Multimodel Machine Learning Framework to Improve Hepatic Fibrosis Grading Using Ultrasound Elastography Systems from Different Vendors.

Journal: Ultrasound in medicine & biology
Published Date: Jan 1, 2020

Abstract

The purpose of the work described here was to determine if the diagnostic performance of point and 2-D shear wave elastography (pSWE; 2-DSWE) using shear wave velocity (SWV) with a new machine learning (ML) technique applied to systems from different vendors is comparable to that of magnetic resonance elastography (MRE) in distinguishing non-significant (

Authors

  • Isabelle Durot
    Department of Radiology, School of Medicine, Stanford University, Stanford, California, USA; Institute of Radiology, Cantonal Hospital Aarau, Aarau, Switzerland.
  • Alireza Akhbardeh
    Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.
  • Hersh Sagreiya
    Perelman School of Medicine, University of Pennsylvania, Philadelphia 19104, USA.
  • Andreas M Loening
    Department of Radiology, School of Medicine, Stanford University, Stanford, California, USA.
  • Daniel L Rubin
    Department of Biomedical Data Science, Stanford University School of Medicine Medical School Office Building, Stanford CA 94305-5479.

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