Deep learning enabled brain shunt valve identification using mobile phones.

Journal: Computer methods and programs in biomedicine
Published Date: Oct 1, 2021

Abstract

BACKGROUND AND OBJECTIVE: Accurate information concerning implanted medical devices prior to a Magnetic resonance imaging (MRI) examination is crucial to assure safety of the patient and to address MRI induced unintended changes in device settings. The identification of these devices still remains a very challenging task. In this paper, with the aim of providing a faster device detection, we propose the adoption of deep learning for medical device detection from X-rays.

Authors

  • Sheeba J Sujit
    Department of Diagnostic and Interventional Imaging, McGovern Medical School, University of Texas Health Science Center, Houston, Texas, USA.
  • Eliana Bonfante
    Department of Diagnostic and Interventional Imaging, The University of Texas Health Science Center at Houston, McGovern Medical School, United States.
  • Azin Aein
    Department of Diagnostic and Interventional Imaging, The University of Texas Health Science Center at Houston, McGovern Medical School, United States; Memorial Hermann Hospital, Texas Medical Center, United States.
  • Ivan Coronado
    Department of Diagnostic and Interventional Imaging, McGovern Medical School, University of Texas Health Science Center, Houston, Texas, USA.
  • Roy Riascos-Castaneda
    Department of Diagnostic and Interventional Imaging, The University of Texas Health Science Center at Houston, McGovern Medical School, United States; Memorial Hermann Hospital, Texas Medical Center, United States.
  • Luca Giancardo
    Center for Precision Health, School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, United States.

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