Artificial intelligence-based automated matching of pulmonary nodules on follow-up chest CT.

Journal: European radiology experimental
PMID: 40316834

Abstract

BACKGROUND: The growing demand for follow-up imaging highlights the need for tools supporting the assessment of pulmonary nodules over time. We evaluated the performance of an artificial intelligence (AI)-based system for automated nodule matching.

Authors

  • Nicola Fink
    Department of Radiology, University Hospital, LMU Munich, Munich, Germany. nicola.fink@med.uni-muenchen.de.
  • Jonathan I Sperl
    Siemens Healthineers, Erlangen, Germany.
  • Johannes Rueckel
    Department of Radiology, University Hospital, LMU Munich, Munich, Germany.
  • Theresa Stüber
    Department of Radiology, University Hospital, LMU Munich, Munich, Germany.
  • Sophia S Goller
    Department of Radiology, University Hospital, LMU Munich, Munich, Germany.
  • Jan Rudolph
    Department of Radiology, University Hospital, LMU Munich, Munich, Germany.
  • Felix Escher
    Department of Radiology, University Hospital, LMU Munich, Munich, Germany.
  • Theresia Aschauer
    Department of Radiology, University Hospital, LMU Munich, Munich, Germany.
  • Boj F Hoppe
    Department of Radiology, University Hospital, LMU Munich, Munich, Germany.
  • Jens Ricke
    Department of Radiology, University Hospital Munich, Germany. Electronic address: jens.ricke@med.uni-muenchen.de.
  • Bastian O Sabel
    Department of Radiology, University Hospital, LMU Munich, Munich, Germany.

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