AI-based lumbar central canal stenosis classification on sagittal MR images is comparable to experienced radiologists using axial images.

Journal: European radiology
PMID:

Abstract

OBJECTIVES: The assessment of lumbar central canal stenosis (LCCS) is crucial for diagnosing and planning treatment for patients with low back pain and neurogenic pain. However, manual assessment methods are time-consuming, variable, and require axial MRIs. The aim of this study is to develop and validate an AI-based model that automatically classifies LCCS using sagittal T2-weighted MRIs.

Authors

  • Jasper W van der Graaf
    Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands. jasper.vandergraaf@radboudumc.nl.
  • Liron Brundel
    Department of Orthopedics, Radboud University Medical Center, Nijmegen, The Netherlands.
  • Miranda L van Hooff
    Department of Orthopedics, Radboud University Medical Center, Nijmegen, The Netherlands.
  • Marinus de Kleuver
    Department of Orthopedics, Radboud University Medical Center, Nijmegen, The Netherlands.
  • Nikolas Lessmann
    Image Sciences Institute, University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands. Electronic address: N.Lessmann@umcutrecht.nl.
  • Bas J Maresch
    Department of Radiology, Hospital Gelderse Vallei, Ede, The Netherlands.
  • Myrthe M Vestering
    From the Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, the Netherlands (K.G.v.L., S.S., M.J.C.M.R., M.H., C.M.S.P., M.d.R., B.v.G., B.H.J.G., J.M.); Department of Radiology (M.J.C.M.R.) and Department of MICT and Imaging Techniques (T.S.), Jeroen Bosch Hospital, 's-Hertogenbosch, the Netherlands; Department of Radiology, Meander Medical Centre, Amersfoort, the Netherlands (C.M.S.P., M.V.); Department of Radiology, Hospital Gelderse Vallei, Ede, the Netherlands (B.M., M.M.V.); Department of Radiology, Noordwest Ziekenhuisgroep, Alkmaar, the Netherlands (C.F.v.D., P.A.); Department of Radiology & Nuclear Medicine, Máxima Medical Center, Eindhoven, the Netherlands (E.L.K., F.v.d.W.); Department of Radiology, Ziekenhuisgroep Twente, Almelo, the Netherlands (E.V.H., F.M.t.B., M.M., O.V., Y.H.G.v.B.F.); Center for Radiology and Nuclear Medicine, Deventer Hospital, Deventer, the Netherlands (E.L.V., J.M.L., M.N.); Department of Radiology, Catharina Hospital, Eindhoven, the Netherlands (E.M.J.M., J.N., K.M.E.M.); Department of Radiology, University Medical Center Utrecht, Utrecht, the Netherlands (F.A.A.M.H.); Department of Radiology, Zaans Medisch Centrum, Zaandam, the Netherlands (F.v.H.); Department of Radiology and Nuclear Medicine, Amsterdam UMC-Location University of Amsterdam, Amsterdam, the Netherlands (I.A.H.v.d.B.); Department of Radiology & Nuclear Medicine, Haaglanden Medical Center, The Hague, the Netherlands (J.H.); Department of Radiology, Amsterdam University Medical Center, Amsterdam, the Netherlands (J.I.M.L.V.); Department of Radiology and Nuclear Medicine, Rijnstate, Arnhem, the Netherlands (L.N.D.); Department of Radiology, St Antonius Hospital, Nieuwegein, the Netherlands (L.C.M.L., S.A.); Department of Radiology, Isala Hospital, Zwolle, the Netherlands (M.F.B.); and Department of Radiology, Groene Hart Hospital, Gouda, the Netherlands (S.M.B.).
  • Jacco Spermon
    Department of Radiology, Hospital Gelderse Vallei, Ede, The Netherlands.
  • Bram van Ginneken
    Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands; Fraunhofer Mevis, Bremen, Germany.
  • Matthieu J C M Rutten
    Department of Medical Imaging, Radboud university medical center, P.O. Box 9101, 6500 HB, Nijmegen, The Netherlands.