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Orthopaedic Proceedings
Vol. 106-B, Issue SUPP_15 | Pages 31 - 31
7 Aug 2024
Williams J Meakin J Whitehead N Mills A Williams D Ward M Kelly E Shillabeer D Javadi A Holsgrove T Holt C
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Background

Our current research aims to develop technologies to predict spinal loads in vivo using a combination of imaging and modelling methods. To ensure the project's success and inform future applications of the technology, we sought to understand the opinions and perspectives of patients and the public.

Methods

A 90-minute public and patient involvement event was developed in collaboration with Exeter Science Centre and held on World Spine Day 2023. The event involved a brief introduction to the project goals followed by an interactive questionnaire to gauge the participants’ background knowledge and interest. The participants then discussed five topics: communication, future directions of the research, concerns about the research protocol, concerns about data, and interest in the project team and research process. A final questionnaire was used to determine their thoughts about the event.


Orthopaedic Proceedings
Vol. 105-B, Issue SUPP_16 | Pages 50 - 50
17 Nov 2023
Williams D Ward M Kelly E Shillabeer D Williams J Javadi A Holsgrove T Meakin J Holt C
Full Access

Abstract

Objectives

Spinal disorders such as back pain incur a substantial societal and economic burden. Unfortunately, there is lack of understanding and treatment of these disorders are further impeded by the inability to assess spinal forces in vivo. The aim of this project is to address this challenge by developing and testing a novel image-driven approach that will assess the forces in an individual's spine in vivo by incorporating information acquired from multimodal imaging (magnetic resonance imaging (MRI) and biplane X-rays) in a subject-specific model.

Methods

Magnetic resonance and biplane X-ray imaging are used to capture information about the anatomy, tissues, and motion of an individual's spine as they perform a range of everyday activities. This information is then utilised in a subject-specific computational model based on the finite element method to predict the forces in their spine. The project is also utilising novel machine learning algorithms and in vitro, six-axis mechanical testing on human, porcine and bovine samples to develop and test the modelling methods rigorously.