In this section
Based at 51³Ô¹ÏÍø's White City Campus, we are a research group with a focus on Spine Biomechanics. We use a range of tools to better understanding in the areas of spinal injury, spinal deformity and spinal surgery.
Our lab has state-of-the-art ex vivo testing capabilities, including bespoke testing rigs, a 6 DOF robot arm, a C-arm, pressure needles, water baths, and high-speed X-ray. We also have access to advanced imaging technologies, including micro-CT, 9.4T MRI, and microscopy.
We use novel computational approaches (finite element modelling, msk modelling, digital volume correlation (DVC), machine learning) to develop workflows to provide clinicians with information to inform patient treatment strategies, to better predict risk of injury, and to assess scoliosis brace designs.
We collaborate globally, with ongoing projects with colleagues in New Zealand, USA, Portugal, South Africa, Germany, Australia, Sri Lanka and India.
You can explore our recent publications below.
@article{Wai:2026,
author = {Wai, G and Raftery, KA and Lali, F and Rodrigues, L and Knapp, KM and Meakin, JR and Newell, N},
journal = {Frontiers in Bioengineering and Biotechnology},
title = {Statistical shape modelling of the human lumbar spine: a scoping review characterising the research landscape and potential clinical applications},
url = {https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2026.1884249/abstract},
year = {2026}
}
TY - JOUR
AB - Objective: A systematic scoping review was conducted to characterise the current research landscape of statistical shape modelling of the lumbar spine, focusing on the proposed clinical applications of the method. Methods: MEDLINE, Embase, IEEE Xplore, and Scopus were searched for sources published before December 2024. Sources were screened against predefined inclusion and exclusion criteria and data were extracted using a structured form in Covidence. Results: 2,363 records were identified for review. Following duplicate removal and screening, 78 studies remained for data extraction. Most studies were exploratory (59%) or descriptive (13%) in nature. The vertebral body was the most frequently isolated region, with L5 often excluded due to its anatomical distinctiveness. Reporting of population demographics and dataset composition was highly inconsistent, highlighting substantial heterogeneity in both methodology and population selection. Conclusion: The use of lumbar spine statistical shape models (SSMs) has been predominantly exploratory or descriptive, and clinical evaluation of the method remains limited to early benchmarking against gold standard methods. In the short term, SSMs are well suited to exploratory analyses of clinical populations, revealing patterns not captured by conventional measures. Progress will benefit from training data aligned to defined clinical questions, adequate sample sizes and demographic breadth, and consistent reporting of validation metrics such as compactness, generalisability and specificity. This will enable development towards a technology that can lead to meaningful clinical translation.
AU - Wai,G
AU - Raftery,KA
AU - Lali,F
AU - Rodrigues,L
AU - Knapp,KM
AU - Meakin,JR
AU - Newell,N
PY - 2026///
SN - 2296-4185
TI - Statistical shape modelling of the human lumbar spine: a scoping review characterising the research landscape and potential clinical applications
T2 - Frontiers in Bioengineering and Biotechnology
UR - https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2026.1884249/abstract
ER -