Estimating lumbar bone mineral density from conventional MRI and radiographs with deep learning in spine patients.

Journal: European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society
PMID: 39212711

Abstract

PURPOSE: This study aimed to develop machine learning methods to estimate bone mineral density and detect osteopenia/osteoporosis from conventional lumbar MRI (T1-weighted and T2-weighted images) and planar radiography in combination with clinical data and imaging parameters of the acquisition protocol.

Authors

  • Fabio Galbusera
    Laboratory of Biological Structures Mechanics, IRCCS Istituto Ortopedico Galeazzi, Via Galeazzi 4, 20161, Milan, Italy. fabio.galbusera@grupposandonato.it.
  • Andrea Cina
    IRCCS Istituto Ortopedico Galeazzi, Via Riccardo Galeazzi 4, 20161, Milan, Italy.
  • Dave O'Riordan
    Department of Teaching, Research and Development, Schulthess Clinic, Lengghalde 2, Zurich, 8008, Switzerland.
  • Jacopo A Vitale
    Department of Teaching, Research and Development, Schulthess Clinic, Lengghalde 2, Zurich, 8008, Switzerland.
  • Markus Loibl
    Department of Spine Surgery and Neurosurgery, Schulthess Klinik, Zurich, Switzerland.
  • Tamás F Fekete
    Department of Teaching, Research and Development, Schulthess Clinic, Lengghalde 2, Zurich, 8008, Switzerland.
  • Frank Kleinstück
    Spine Center Division, Department of Orthopedics and Neurosurgery, Schulthess Klinik, Zurich, Switzerland.
  • Daniel Haschtmann
    Department of Teaching, Research and Development, Schulthess Clinic, Lengghalde 2, Zurich, 8008, Switzerland.
  • Anne F Mannion
    Department of Teaching, Research and Development, Schulthess Klinik, Zurich, Switzerland.

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