Literature DB >> 29625748

Engineering approaches to understanding mechanisms of spinal column injury leading to spinal cord injury.

Claire F Jones1, Elizabeth C Clarke2.   

Abstract

BACKGROUND: The mechanical interactions occurring between the spinal column and spinal cord during an injury event are complex and variable, and likely have implications for the clinical presentation and prognosis of the individual.
METHODS: The engineering approaches that have been developed to better understand spinal column and cord interactions during an injury event are discussed. These include injury models utilising human and animal cadaveric specimens, in vivo anaesthetised animals, finite element models, inanimate physical systems and combinations thereof.
FINDINGS: The paper describes the development of these modelling approaches, discusses the advantages and disadvantages of the various models, and the major outcomes that have had implications for spinal cord injury research and clinical practice.
INTERPRETATION: The contribution of these four engineering approaches to understanding the interaction between the biomechanics and biology of spinal cord injury is substantial; they have improved our understanding of the factors contributing to the spinal column disruption, the degree of spinal cord deformation or motion, and the resultant neurological deficit and imaging features. Models of the injury event are challenging to produce, but technological advances are likely to improve these models and, consequently, our understanding of the mechanical context in which the biological injury occurs.
Copyright © 2018 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Animal model; Cadaveric; Closed column; Finite element model; Spinal cord injury; Surrogate

Mesh:

Year:  2018        PMID: 29625748     DOI: 10.1016/j.clinbiomech.2018.03.019

Source DB:  PubMed          Journal:  Clin Biomech (Bristol, Avon)        ISSN: 0268-0033            Impact factor:   2.063


  1 in total

1.  Spine Medical Image Segmentation Based on Deep Learning.

Authors:  Qingfeng Zhang; Yun Du; Zhiqiang Wei; Hengping Liu; Xiaoxia Yang; Dongfang Zhao
Journal:  J Healthc Eng       Date:  2021-12-15       Impact factor: 2.682

  1 in total

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