Deep Learning-driven classification of external DICOM studies for PACS archiving.

Journal: European radiology
Published Date: Jul 5, 2022

Abstract

OBJECTIVES: Over the course of their treatment, patients often switch hospitals, requiring staff at the new hospital to import external imaging studies to their local database. In this study, the authors present MOdality Mapping and Orchestration (MOMO), a Deep Learning-based approach to automate this mapping process by combining metadata analysis and a neural network ensemble.

Authors

  • Frederic Jonske
    Computer Algorithms for Medicine Laboratory, Graz, Styria, Austria; Institute for AI in Medicine (IKIM), University Medicine Essen, Girardetstraße 2, 45131 Essen, Germany.
  • Maximilian Dederichs
    Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany.
  • Moon-Sung Kim
    Institute of AI in Medicine (IKIM), University Hospital Essen, Girardetstraße 2, 45131, Essen, Germany.
  • Julius Keyl
    Institute of AI in Medicine (IKIM), University Hospital Essen, Girardetstraße 2, 45131, Essen, Germany.
  • Jan Egger
    Institute for Computer Graphics and Vision, Faculty of Computer Science and Biomedical Engineering, Graz University of Technology, Graz, Austria.
  • Lale Umutlu
    Institute of Diagnostic and Interventional Radiology and Neuroradiology, Departments of.
  • Michael Forsting
    Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany.
  • Felix Nensa
    Institute for AI in Medicine (IKIM), University Hospital Essen, 45131 Essen, Germany.
  • Jens Kleesiek
    AG Computational Radiology, Abteilung Radiologie, Deutsches Krebsforschungszentrum (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Deutschland. j.kleesiek@dkfz-heidelberg.de.

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