Radiological age assessment based on clavicle ossification in CT: enhanced accuracy through deep learning.

Journal: International journal of legal medicine
PMID: 38286953

Abstract

BACKGROUND: Radiological age assessment using reference studies is inherently limited in accuracy due to a finite number of assignable skeletal maturation stages. To overcome this limitation, we present a deep learning approach for continuous age assessment based on clavicle ossification in computed tomography (CT).

Authors

  • Philipp Wesp
    From the Department of Radiology, University Hospital, LMU Munich.
  • Balthasar Maria Schachtner
    Department of Radiology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany.
  • Katharina Jeblick
    Department of Radiology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany.
  • Johanna Topalis
    Department of Radiology, LMU University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany.
  • Marvin Weber
    Institute of Informatics, LMU Munich, Oettingenstraße 67, 80538, Munich, Germany.
  • Florian Fischer
    Institute of Forensic Medicine, LMU Munich, Nußbaumstraße 26, 80336, Munich, Germany.
  • Randolph Penning
    Institute of Forensic Medicine, LMU Munich, Nußbaumstraße 26, 80336, Munich, Germany.
  • Jens Ricke
    Department of Radiology, University Hospital Munich, Germany. Electronic address: jens.ricke@med.uni-muenchen.de.
  • Michael Ingrisch
    Department of Radiology, Ludwig-Maximilians-University Munich, Munich, Germany.
  • Bastian Oliver Sabel
    From the Department of Radiology, University Hospital, LMU Munich.

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