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{Lali:2026:10.1097/brs.0000000000005698,
author = {Lali, F and Raftery, K and Levy, H and Freedman, B and Newell, N},
doi = {10.1097/brs.0000000000005698},
journal = {Spine},
title = {Patient specific finite element modelling outputs outperform clinical metrics in predicting fusion cage subsidence},
url = {http://dx.doi.org/10.1097/brs.0000000000005698},
year = {2026}
}
TY - JOUR
AB - Study Design. Finite element (FE) analysis of retrospective clinical cohort.Objective. To determine whether preoperative CT-derived FE model outputs can improve subsidence prediction compared to conventional clinical measurements alone in patients undergoing transforaminal lumbar interbody fusion (TLIF).Summary of Background Data. Cage subsidence occurs in approximately 20% of spinal fusion patients and can lead to complications requiring reoperation. While individual risk factors are known, no validated tool integrates patient anatomy, bone quality, and implant characteristics to predict subsidence. Finite element models have been hypothesized to predict subsidence but lack clinical validation.Methods. Patient-specific FE models were created from preoperative CT scans of 42 TLIF patients: N=22 Severe subsidence (≥4 mm); N=20 Non-severe subsidence (<4 mm). Vertebral geometries were segmented, and bone material properties were assigned based on Hounsfield units (HU). Cage positions from postoperative scans were registered to preoperative anatomy. Endplate and trabecular stresses and strains from FE models were compared to clinical measures using receiver operating characteristic analysis.Results. 15 Principal stresses and strains of the FE simulations showed significantly higher values in severely subsided patients compared to the Non-Severe group. Average trabecular intermediate strain achieved the highest area under curve score (AUC=0.809), outperforming all clinical metrics. Peak endplate minimum principal stress (AUC=0.775) was the second-best FE classifier. Traditional clinical measures showed lower discriminative ability: cage length (AUC=0.797), cage width (AUC=0.750), and cage height (AUC=0.698).Conclusion. Patient-specific FE model outputs significantly correlate with clinical subsidence outcomes and outperform several traditional metrics in classifying severe subsidence. Both endplate and trabecular stresses and strains are important predictors, with
AU - Lali,F
AU - Raftery,K
AU - Levy,H
AU - Freedman,B
AU - Newell,N
DO - 10.1097/brs.0000000000005698
PY - 2026///
SN - 0362-2436
TI - Patient specific finite element modelling outputs outperform clinical metrics in predicting fusion cage subsidence
T2 - Spine
UR - http://dx.doi.org/10.1097/brs.0000000000005698
UR - https://doi.org/10.1097/brs.0000000000005698
ER -