Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists.

Journal: PLoS medicine
Published Date: Nov 20, 2018

Abstract

BACKGROUND: Chest radiograph interpretation is critical for the detection of thoracic diseases, including tuberculosis and lung cancer, which affect millions of people worldwide each year. This time-consuming task typically requires expert radiologists to read the images, leading to fatigue-based diagnostic error and lack of diagnostic expertise in areas of the world where radiologists are not available. Recently, deep learning approaches have been able to achieve expert-level performance in medical image interpretation tasks, powered by large network architectures and fueled by the emergence of large labeled datasets. The purpose of this study is to investigate the performance of a deep learning algorithm on the detection of pathologies in chest radiographs compared with practicing radiologists.

Authors

  • Pranav Rajpurkar
    Harvard Medical School, Department of Biomedical Informatics, Cambridge, MA, 02115, US.
  • Jeremy Irvin
    Department of Computer Science, Stanford University, Stanford, California, United States of America.
  • Robyn L Ball
    From the Department of Radiology, Stanford University School of Medicine, Stanford University Medical Center, 725 Welch Rd, Room 1675, Stanford, Calif 94305-5913 (M.C.C., N.M., D.B.L., C.P.L., M.P.L.); Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, Calif (R.L.B., L.Y.); Department of Bioinformatics, University of Utah Medical Center, Salt Lake City, Utah (B.E.C.); and Department of Radiology, Duke University Medical Center, Durham, NC (T.J.A.).
  • Kaylie Zhu
    Department of Computer Science, Stanford University, Stanford, California, United States of America.
  • Brandon Yang
    Department of Computer Science, Stanford University, Stanford, California, United States of America.
  • Hershel Mehta
    Department of Computer Science, Stanford University, Stanford, California, United States of America.
  • Tony Duan
    Department of Computer Science, Stanford University, Stanford, California, United States of America.
  • Daisy Ding
    Department of Computer Science, Stanford University, Stanford, California, United States of America.
  • Aarti Bagul
    Department of Computer Science, Stanford University, Stanford, California, United States of America.
  • Curtis P Langlotz
    Stanford University, University Medical Line, Stanford, CA, 94305, US.
  • Bhavik N Patel
    Department of Radiology, Stanford University, Stanford, California, United States of America.
  • Kristen W Yeom
    Department of Radiology, Stanford University, Stanford, California, United States of America.
  • Katie Shpanskaya
    Department of Radiology, Stanford University, Stanford, California, United States of America.
  • Francis G Blankenberg
    Department of Radiology, Stanford University, Stanford, California, United States of America.
  • Jayne Seekins
    Department of Radiology, Stanford University, Stanford, California, United States of America.
  • Timothy J Amrhein
    3 Department of Radiology, Duke University Medical Center, Durham, NC.
  • David A Mong
    Department of Radiology, University of Colorado, Denver, Colorado, United States of America.
  • Safwan S Halabi
  • Evan J Zucker
    Department of Radiology, Stanford University, Stanford, California, United States of America.
  • Andrew Y Ng
  • Matthew P Lungren

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