Addressing the Contrast Media Recognition Challenge: A Fully Automated Machine Learning Approach for Predicting Contrast Phases in CT Imaging.

Journal: Investigative radiology
PMID: 38436405

Abstract

OBJECTIVES: Accurately acquiring and assigning different contrast-enhanced phases in computed tomography (CT) is relevant for clinicians and for artificial intelligence orchestration to select the most appropriate series for analysis. However, this information is commonly extracted from the CT metadata, which is often wrong. This study aimed at developing an automatic pipeline for classifying intravenous (IV) contrast phases and additionally for identifying contrast media in the gastrointestinal tract (GIT).

Authors

  • Giulia Baldini
    Institute for Artificial Intelligence in Medicine, University Medicine Essen, Essen, Germany.
  • René Hosch
    Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Germany.
  • Cynthia S Schmidt
  • Katarzyna Borys
    Institute for Artificial Intelligence in Medicine, University Hospital Essen, Girardetstraße 2, 45131 Essen, Germany; Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45147 Essen, Germany. Electronic address: Katarzyna.Borys@uk-essen.de.
  • Lennard Kroll
    Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Germany.
  • Sven Koitka
    Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany.
  • Patrizia Haubold
    Department of Diagnostic and Interventional Radiology, Kliniken Essen-Mitte, Essen, Germany.
  • Obioma Pelka
    Department of Computer Science, University of Applied Sciences and Arts Dortmund (FHDO), Dortmund, NRW Germany.
  • Felix Nensa
    Institute for AI in Medicine (IKIM), University Hospital Essen, 45131 Essen, Germany.
  • Johannes Haubold
    Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany. Johannes.haubold@uk-essen.de.

Keywords

External Resources

Popular Topics
Recent Journals