Development of a Deep Learning Model for Classification of Hepatic Steatosis from Clinical Standard Ultrasound.

Journal: Ultrasound in medicine & biology
PMID:

Abstract

OBJECTIVE: Early detection and monitoring of hepatic steatosis can help establish appropriate preventative measures against progression to more advanced disease. We aimed to develop a deep learning (DL) program for classification of hepatic steatosis from standard-of-care grayscale ultrasound (US) images.

Authors

  • Ahmed El Kaffas
    Department of Radiology, University of California San Diego School of Medicine, La Jolla, CA, USA.
  • Krishna Chaitanya Bhatraju
    Department of Radiology, University of California San Diego School of Medicine, La Jolla, CA, USA; Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.
  • Jenny M Vo-Phamhi
    Department of Radiology, University of California San Diego School of Medicine, La Jolla, CA, USA; Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.
  • Thodsawit Tiyarattanachai
    Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
  • Neha Antil
    Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.
  • Lindsey M Negrete
    Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.
  • Aya Kamaya
    Department of Radiology, School of Medicine, Stanford University, Stanford, California 94305.
  • Luyao Shen
    Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA. Electronic address: lyshen@stanford.edu.