Assessment of inspiration and technical quality in anteroposterior thoracic radiographs using machine learning.

Journal: Radiography (London, England : 1995)
Published Date: Nov 29, 2023

Abstract

INTRODUCTION: Chest radiographs are the most performed radiographic procedure, but suboptimal technical factors can impact clinical interpretation. A deep learning model was developed to assess technical and inspiratory adequacy of anteroposterior chest radiographs.

Authors

  • L Sorace
    Department of Radiology, Austin Hospital, Heidelberg, Australia. Electronic address: laurence.sorace@icloud.com.
  • N Raju
    Department of Radiology, Austin Hospital, Heidelberg, Australia.
  • J O'Shaughnessy
    Department of Radiology, Austin Hospital, Heidelberg, Australia.
  • S Kachel
    Department of Radiology, Austin Hospital, Heidelberg, Australia; The University of Melbourne, Parkville, Australia; Columbia University, New York, NY, USA.
  • K Jansz
    Department of Radiology, Austin Hospital, Heidelberg, Australia.
  • N Yang
    Department of Radiology, Austin Hospital, Heidelberg, Australia; The University of Melbourne, Parkville, Australia.
  • R P Lim
    Department of Radiology, Austin Hospital, Heidelberg, Australia; The University of Melbourne, Parkville, Australia.

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