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Review
. 2020 Aug;19(4):1169-1185.
doi: 10.1007/s10237-020-01367-8. Epub 2020 Jul 16.

Machine learning methods to support personalized neuromusculoskeletal modelling

David J Saxby #  1 , Bryce Adrian Killen #  2 , C Pizzolato  3 , C P Carty  3   4 , L E Diamond  3 , L Modenese  5 , J Fernandez  6 , G Davico  7   8 , M Barzan  3 , G Lenton  3 , S Brito da Luz  3 , E Suwarganda  3 , D Devaprakash  3 , R K Korhonen  9 , J A Alderson  10 , T F Besier  6 , R S Barrett  3 , D G Lloyd  3
Affiliations
Review

Machine learning methods to support personalized neuromusculoskeletal modelling

David J Saxby et al. Biomech Model Mechanobiol. 2020 Aug.

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.

Keywords: Artificial intelligence; Biomechanics; Computational models; Musculoskeletal.

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