Practical applications of deep learning: classifying the most common categories of plain radiographs in a PACS using a neural network.

Journal: European radiology
Published Date: Sep 28, 2020

Abstract

OBJECTIVES: The goal of the present study was to classify the most common types of plain radiographs using a neural network and to validate the network's performance on internal and external data. Such a network could help improve various radiological workflows.

Authors

  • Thomas Dratsch
    Institute of Diagnostic and Interventional Radiology, University Hospital Cologne, Kerpener Str. 62, 50937, Cologne, Germany. t.dratsch@mac.comn.
  • Michael Korenkov
    Institute of Diagnostic and Interventional Radiology, University Hospital Cologne, Kerpener Str. 62, 50937, Cologne, Germany.
  • David Zopfs
    Institute for Diagnostic and Interventional Radiology, University Hospital Cologne, Cologne, Germany.
  • Sebastian Brodehl
    Institute of Computer Science, Johannes Gutenberg University Mainz, Mainz, Germany.
  • Bettina Baeßler
    Department of Radiology, University Hospital of Cologne, Cologne, Germany.
  • Daniel Giese
    Institute of Diagnostic and Interventional Radiology, University Hospital Cologne, Kerpener Str. 62, 50937, Cologne, Germany.
  • Sebastian Brinkmann
    Department of General, Visceral and Cancer Surgery, University Hospital Cologne, Cologne, Germany.
  • David Maintz
    Institute for Diagnostic and Interventional Radiology, University Hospital Cologne, Cologne, Germany.
  • Daniel Pinto Dos Santos
    Department of Diagnostic and Interventional Radiology, University Hospital Cologne, Cologne, Germany.

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