A Deep Learning Model for the Accurate and Reliable Classification of Disc Degeneration Based on MRI Data.

Journal: Investigative radiology
Published Date:

Abstract

OBJECTIVES: Although magnetic resonance imaging-based formalized grading schemes for intervertebral disc degeneration offer improved reproducibility compared with purely subjective ratings, their intrarater and interrater reliability are not nearly good enough to be able to detect small to medium effects in clinical longitudinal studies. The aim of this study thus was to develop a method that enables automatic and therefore reproducible and reliable evaluation of disc degeneration based on conventional clinical image data and Pfirrmann's grading scheme.

Authors

  • Frank Niemeyer
    Institute of Orthopedic Research and Biomechanics, Center for Trauma Research Ulm, Ulm University, Ulm, Germany.
  • Fabio Galbusera
    Laboratory of Biological Structures Mechanics, IRCCS Istituto Ortopedico Galeazzi, Via Galeazzi 4, 20161, Milan, Italy. fabio.galbusera@grupposandonato.it.
  • Youping Tao
    From the Institute for Orthopaedic Research and Biomechanics, University Hospital Ulm, Ulm, Germany.
  • Annette Kienle
    SpineServ GmbH & Co. KG.
  • Meinrad Beer
    Diagnostic and Interventional Radiology, University Hospital Ulm, Germany.
  • Hans-Joachim Wilke
    Institute of Orthopaedic Research and Biomechanics, Trauma Research Center Ulm, University Hospital Ulm, Helmholtzstraße 14, Ulm 89081, Germany. Electronic address: hans-joachim.wilke@uni-ulm.de.