Can AI outperform a junior resident? Comparison of deep neural network to first-year radiology residents for identification of pneumothorax.

Journal: Emergency radiology
Published Date: Jul 8, 2020

Abstract

PURPOSE: To (1) develop a deep learning system (DLS) using a deep convolutional neural network (DCNN) for identification of pneumothorax, (2) compare its performance to first-year radiology residents, and (3) evaluate the ability of a DLS to augment radiology residents by detecting missed pneumothoraces.

Authors

  • Paul H Yi
    The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland. Electronic address: Pyi10@jhmi.edu.
  • Tae Kyung Kim
    The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 601 N. Caroline St., Room 4223, Baltimore, MD, 21287, USA.
  • Alice C Yu
    The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 601 N. Caroline St., Room 4223, Baltimore, MD, 21287, USA.
  • Bradford Bennett
    Radiology Artificial Intelligence Lab (RAIL), Malone Center for Engineering in Healthcare, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, USA.
  • John Eng
    Radiology Artificial Intelligence Lab (RAIL), Malone Center for Engineering in Healthcare, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, USA.
  • Cheng Ting Lin
    The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA. clin97@jhmi.edu.

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