Assessment of knee pain from MR imaging using a convolutional Siamese network.

Journal: European radiology
PMID: 32055951

Abstract

OBJECTIVES: It remains difficult to characterize the source of pain in knee joints either using radiographs or magnetic resonance imaging (MRI). We sought to determine if advanced machine learning methods such as deep neural networks could distinguish knees with pain from those without it and identify the structural features that are associated with knee pain.

Authors

  • Gary H Chang
    Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, 72 E. Concord Street, Evans 636, Boston, MA, 02118, USA.
  • David T Felson
    Section of Rheumatology, Department of Medicine, Boston University School of Medicine, Boston, MA, 02118, USA.
  • Shangran Qiu
    Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, 72 E. Concord Street, Evans 636, Boston, MA, 02118, USA.
  • Ali Guermazi
    Department of Radiology, Boston University School of Medicine, Boston, MA, 02118, USA.
  • Terence D Capellini
    Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, 02138, USA.
  • Vijaya B Kolachalama
    1Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, MA 02118 USA.

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