Dataset of Finite Element Models of Normal and Deformed Thoracolumbar Spine.

Adult spine deformity (ASD) is prevalent and leads to a sagittal misalignment in the vertebral column. Computational methods, including Finite Element (FE) Models, have emerged as valuable tools for investigating the causes and treatment of ASD through biomechanical simulations. However, the process of generating personalised FE models is often complex and time-consuming. To address this challenge, we present a dataset of FE models with diverse spine morphologies that statistically represent real geometries from a cohort of patients. These models are generated using EOS images, which are utilized to reconstruct 3D surface spine models. Subsequently, a Statistical Shape Model (SSM) is constructed, enabling the adaptation of a FE hexahedral mesh template for both the bone and soft tissues of the spine through mesh morphing. The SSM deformation fields facilitate the personalization of the mean hexahedral FE model based on sagittal balance measurements. Ultimately, this new hexahedral SSM tool offers a means to generate a virtual cohort of 16807 thoracolumbar FE spine models, which are openly shared in a public repository.

Proximal Junction Failure in Spine Surgery: Integrating Geometrical and Biomechanical Global Descriptors Improves GAP Score-Based Assessment.

STUDY DESIGN: Retrospective observational study. OBJECTIVE: Biomechanical and geometrical descriptors are used to improve global alignment and proportion (GAP) prediction accuracy to detect proximal junctional failure (PJF). SUMMARY OF BACKGROUND DATA: PJF is probably the most important complication after sagittal imbalance surgery. The GAP score has been introduced as an effective predictor for PJF, but it fails in certain situations. In this study, 112 patient records were gathered (57 PJF; 55 controls) with biomechanical and geometrical descriptors measured to stratify control and failure cases. PATIENTS AND METHODS: Biplanar EOS radiographs were used to build 3-dimensional full-spine models and determine spinopelvic sagittal parameters. The bending moment (BM) was calculated as the upper body mass times, the effective distance to the body center of mass at the adjacent upper instrumented vertebra +1. Other geometrical descriptors such as full balance index (FBI), spino-sacral angle (SSA), C7 plumb line/sacrofemoral distance ratio (C7/SFD ratio), T1-pelvic angle (TPA), and cervical inclination angle (CIA) were also evaluated. The respective abilities of the GAP, FBI, SSA, C7/SFD, TPA, CIA, body weight, body mass index, and BM to discriminate PJF cases were analyzed through receiver operating characteristic curves and corresponding areas under the curve (AUC). RESULTS: GAP (AUC = 0.8816) and FBI (AUC = 0.8933) were able to discriminate PJF cases but the highest discrimination power (AUC = 0.9371) was achieved with BM at upper instrumented vertebra + 1. Parameter cutoff analyses provided quantitative thresholds to characterize the control and failure groups and led to improved PJF discrimination, with GAP and BM being the most important contributors. SSA (AUC = 0.2857), C7/SFD (AUC = 0.3143), TPA (AUC = 0.5714), CIA (AUC = 0.4571), body weight (AUC = 0.6319), and body mass index (AUC = 0.7716) did not adequately predict PJF. CONCLUSION: BM reflects the quantitative biomechanical effect of external loads and can improve GAP accuracy. Sagittal alignments and mechanical integrated scores could be used to better prognosticate the risk of PJF.