Muscle magnetic resonance characterization of STIM1 tubular aggregate myopathy using unsupervised learning.

Journal: PloS one
PMID: 37155641

Abstract

PURPOSE: Congenital myopathies are a heterogeneous group of diseases affecting the skeletal muscles and characterized by high clinical, genetic, and histological variability. Magnetic Resonance (MR) is a valuable tool for the assessment of involved muscles (i.e., fatty replacement and oedema) and disease progression. Machine Learning is becoming increasingly applied for diagnostic purposes, but to our knowledge, Self-Organizing Maps (SOMs) have never been used for the identification of the patterns in these diseases. The aim of this study is to evaluate if SOMs may discriminate between muscles with fatty replacement (S), oedema (E) or neither (N).

Authors

  • Amalia Lupi
    Institute of Radiology, Department of Medicine-DIMED, University of Padua, Padua, Italy.
  • Simone Spolaor
    Microsystems, Department of Mechanical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
  • Alessandro Favero
    Institute of Radiology, Department of Medicine-DIMED, University of Padua, Padua, Italy.
  • Luca Bello
    Department of Neurosciences, University of Padua, Padua, Italy.
  • Roberto Stramare
    Clinical and Translational Advanced Imaging Unit, Department of Medicine-DIMED, University of Padua, Padua, Italy.
  • Elena Pegoraro
    Department of Neurosciences, University of Padua, Padua, Italy.
  • Marco Salvatore Nobile
    Department of Environmental Sciences, Informatics and Statistics (DAIS), Ca' Foscari University of Venice, Venice, Italy.

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