Deep learning for automated, interpretable classification of lumbar spinal stenosis and facet arthropathy from axial MRI.

Journal: European radiology
PMID: 36920520

Abstract

OBJECTIVES: To evaluate a deep learning model for automated and interpretable classification of central canal stenosis, neural foraminal stenosis, and facet arthropathy from lumbar spine MRI.

Authors

  • Upasana Upadhyay Bharadwaj
    Department of Radiology and Biomedical Imaging, University of California San Francisco, 1700 4th Street, Byers Hall, Suite 203, Room 203D, San Francisco, CA 94158, USA.
  • Miranda Christine
    Department of Radiology and Biomedical Imaging, University of California San Francisco, 185 Berry Street, Suite 350, San Francisco, CA, 94107, USA.
  • Steven Li
    Department of Radiology and Biomedical Imaging, University of California San Francisco, 185 Berry Street, Suite 350, San Francisco, CA, 94107, USA.
  • Dean Chou
    Department of Neurosurgery, University of California, San Francisco, California, USA.
  • Valentina Pedoia
    Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA.
  • Thomas M Link
    Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA.
  • Cynthia T Chin
    Department of Radiology and Biomedical Imaging, University of California San Francisco, 505 Parnassus Avenue, Box 0628, San Francisco, CA 94143, USA. Electronic address: cynthia.t.chin@ucsf.edu.
  • Sharmila Majumdar
    Department of Radiology and Biomedical Imaging, University of California San Francisco, 1700 4th Street, Byers Hall, Suite 203, Room 203D, San Francisco, CA 94158, USA.

Keywords

External Resources

Popular Topics
Recent Journals