Automated detection of moderate and large pneumothorax on frontal chest X-rays using deep convolutional neural networks: A retrospective study.

Journal: PLoS medicine
Published Date: Nov 20, 2018

Abstract

BACKGROUND: Pneumothorax can precipitate a life-threatening emergency due to lung collapse and respiratory or circulatory distress. Pneumothorax is typically detected on chest X-ray; however, treatment is reliant on timely review of radiographs. Since current imaging volumes may result in long worklists of radiographs awaiting review, an automated method of prioritizing X-rays with pneumothorax may reduce time to treatment. Our objective was to create a large human-annotated dataset of chest X-rays containing pneumothorax and to train deep convolutional networks to screen for potentially emergent moderate or large pneumothorax at the time of image acquisition.

Authors

  • Andrew G Taylor
    Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA.
  • Clinton Mielke
    Center for Digital Health Innovation, University of California, San Francisco, San Francisco, California, United States of America.
  • John Mongan
    From the Departments of Urology (T.C., M.U., H.C.C., M.S.) and Radiology and Biomedical Imaging (J.M., M.P.K., A.T., P.J., R.G., S.W.), University of California, San Francisco. 505 Parnassus Ave, M-391, San Francisco, CA 94143; and Division of Urology, Faculty of Medicine, King Chulalongkorn Memorial Hospital, Chulalongkorn University, The Thai Red Cross Society, Bangkok, Thailand (M.U.).

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