Machine learning methods to support personalized neuromusculoskeletal modelling.

Journal: Biomechanics and modeling in mechanobiology
PMID: 32676934

Abstract

Many biomedical, orthopaedic, and industrial applications are emerging that will benefit from personalized neuromusculoskeletal models. Applications include refined diagnostics, prediction of treatment trajectories for neuromusculoskeletal diseases, in silico design, development, and testing of medical implants, and human-machine interfaces to support assistive technologies. This review proposes how physics-based simulation, combined with machine learning approaches from big data, can be used to develop high-fidelity personalized representations of the human neuromusculoskeletal system. The core neuromusculoskeletal model features requiring personalization are identified, and big data/machine learning approaches for implementation are presented together with recommendations for further research.

Authors

  • David J Saxby
    Gold Coast Orthopaedics Research, Engineering & Education Alliance (GCORE), Menzies Health Institute Queensland, Griffith University, QLD 4222, Australia.
  • Bryce Adrian Killen
    Human Movement Biomechanics Research Group, KU Leuven, Leuven, Belgium.
  • C Pizzolato
    School of Allied Health Sciences & Gold Coast Orthopaedic Research & Education Alliance (GCORE), Menzies Health Institute Queensland, Griffith University, Gold Coast Campus, Gold Coast, Australia.
  • C P Carty
    School of Allied Health Sciences & Gold Coast Orthopaedic Research & Education Alliance (GCORE), Menzies Health Institute Queensland, Griffith University, Gold Coast Campus, Gold Coast, Australia.
  • L E Diamond
    School of Allied Health Sciences & Gold Coast Orthopaedic Research & Education Alliance (GCORE), Menzies Health Institute Queensland, Griffith University, Gold Coast Campus, Gold Coast, Australia.
  • L Modenese
    Imperial College London, London, UK.
  • J Fernandez
    Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand.
  • G Davico
    Department of Industrial Engineering, University of Bologna, Bologna, Italy.
  • M Barzan
    School of Allied Health Sciences & Gold Coast Orthopaedic Research & Education Alliance (GCORE), Menzies Health Institute Queensland, Griffith University, Gold Coast Campus, Gold Coast, Australia.
  • G Lenton
    School of Allied Health Sciences & Gold Coast Orthopaedic Research & Education Alliance (GCORE), Menzies Health Institute Queensland, Griffith University, Gold Coast Campus, Gold Coast, Australia.
  • S Brito da Luz
    School of Allied Health Sciences & Gold Coast Orthopaedic Research & Education Alliance (GCORE), Menzies Health Institute Queensland, Griffith University, Gold Coast Campus, Gold Coast, Australia.
  • E Suwarganda
    School of Allied Health Sciences & Gold Coast Orthopaedic Research & Education Alliance (GCORE), Menzies Health Institute Queensland, Griffith University, Gold Coast Campus, Gold Coast, Australia.
  • D Devaprakash
    School of Allied Health Sciences & Gold Coast Orthopaedic Research & Education Alliance (GCORE), Menzies Health Institute Queensland, Griffith University, Gold Coast Campus, Gold Coast, Australia.
  • R K Korhonen
    Group for Biophysics of Bone and Cartilage, University of Eastern Finland, Kuopio, Finland.
  • J A Alderson
    University of Western Australia, Perth, Australia.
  • T F Besier
    Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand.
  • R S Barrett
    School of Allied Health Sciences & Gold Coast Orthopaedic Research & Education Alliance (GCORE), Menzies Health Institute Queensland, Griffith University, Gold Coast Campus, Gold Coast, Australia.
  • D G Lloyd
    School of Allied Health Sciences & Gold Coast Orthopaedic Research & Education Alliance (GCORE), Menzies Health Institute Queensland, Griffith University, Gold Coast Campus, Gold Coast, Australia.

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