Artificial Intelligence in Oncological Hybrid Imaging.

Journal: Nuklearmedizin. Nuclear medicine
Published Date:

Abstract

BACKGROUND:  Artificial intelligence (AI) applications have become increasingly relevant across a broad spectrum of settings in medical imaging. Due to the large amount of imaging data that is generated in oncological hybrid imaging, AI applications are desirable for lesion detection and characterization in primary staging, therapy monitoring, and recurrence detection. Given the rapid developments in machine learning (ML) and deep learning (DL) methods, the role of AI will have significant impact on the imaging workflow and will eventually improve clinical decision making and outcomes.

Authors

  • Benedikt Feuerecker
    Department of Radiology, University Hospital, LMU Munich, Munich, Germany.
  • Maurice M Heimer
    Department of Radiology, University Hospital, LMU Munich, Munich, Germany.
  • Thomas Geyer
    Department of Radiology, University Hospital, LMU Munich, Munich, Germany.
  • Matthias P Fabritius
    Department of Radiology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany.
  • Sijing Gu
    Department of Radiology, University Hospital, LMU Munich, Munich, Germany.
  • Balthasar Schachtner
    German Cancer Consortium, Heidelberg, Germany.
  • Leonie Beyer
    Department of Nuclear Medicine, University Hospital, LMU Munich, Munich, Germany.
  • Jens Ricke
    Department of Radiology, University Hospital Munich, Germany. Electronic address: jens.ricke@med.uni-muenchen.de.
  • Sergios Gatidis
    Department of Radiology, Diagnostic and Interventional Radiology, Eberhard Karls University Tübingen, Germany.
  • Michael Ingrisch
    Department of Radiology, Ludwig-Maximilians-University Munich, Munich, Germany.
  • Clemens C Cyran
    Department of Radiology, University Hospital, LMU Munich, Munich, Germany.