Deep learning to automate Brasfield chest radiographic scoring for cystic fibrosis.

Journal: Journal of cystic fibrosis : official journal of the European Cystic Fibrosis Society
Published Date: May 2, 2019

Abstract

BACKGROUND: The aim of this study was to evaluate the hypothesis that a deep convolutional neural network (DCNN) model could facilitate automated Brasfield scoring of chest radiographs (CXRs) for patients with cystic fibrosis (CF), performing similarly to a pediatric radiologist.

Authors

  • Evan J Zucker
    Department of Radiology, Stanford University, Stanford, California, United States of America.
  • Zachary A Barnes
    Department of Computer Science, Stanford University, 353 Serra Mall, Stanford, CA 94305, USA.
  • Matthew P Lungren
  • Yekaterina Shpanskaya
    Department of Radiology, Stanford University School of Medicine, 725 Welch Road, Stanford, CA 94305, USA.
  • Jayne M Seekins
    Department of Radiology, Stanford University School of Medicine, 725 Welch Road, Stanford, CA 94305, USA.
  • Safwan S Halabi
  • David B Larson
    Department of Radiology, Warren Alpert Medical School, Brown University, 593 Eddy St, Providence, RI 02903 (I.P.); Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI (I.P.); Visiana, Hørsholm, Denmark (H.H.T.); Department of Radiology, Stanford University, Palo Alto, Calif (S.S.H., D.B.L.); and Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (J.K.C.).

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