期刊
BIOMECHANICS AND MODELING IN MECHANOBIOLOGY
卷 19, 期 4, 页码 1169-1185出版社
SPRINGER HEIDELBERG
DOI: 10.1007/s10237-020-01367-8
关键词
Computational models; Biomechanics; Artificial intelligence; Musculoskeletal
资金
- Australian Research Council [IC180100024, LP150100905]
- Queensland Motor Accident Insurance Commission (BioSpine)
- MTPConnect BioMedTechHorizons [SLIL_BMTH 07]
- Academy of Finland [286526, 324529]
- Sigrid Juselius Foundation
- IMeasureU Ltd [37122001]
- Cleveland Clinic [1R01EB024573]
- Auckland Bioengineering Institute MedTech [3710225]
- Imperial College Research Fellowship - Imperial College London
- Australian Research Council [IC180100024, LP150100905] Funding Source: Australian Research Council
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.
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