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Finite element modelling of the lumbar spine is a challenging problem. Lower back pain is among the most common pathologies in the global populations, owing to which the patient may need to undergo surgery. The latter may differ in nature and complexity because of spinal disease and patient contraindications (i.e., aging). Today, the understanding of spinal column biomechanics may lead to better comprehension of the disease progression as well as to the development of innovative therapeutic strategies. Better insight into the spine’s biomechanics would certainly guarantee an evolution of current device-based treatments. In this setting, the computational approach appears to be a remarkable tool for simulating physiological and pathological spinal conditions, as well as for various aspects of surgery. Patient-specific computational simulations are constantly evolving, and require a number of validation and verification challenges to be overcome before they can achieve true and accurate results. The aim of the present schematic review is to provide an overview of the evolution and recent advances involved in computational finite element modelling (FEM) of spinal biomechanics and of the fundamental knowledge necessary to develop the best modeling approach in terms of trustworthiness and reliability.

Atlas-based analyses of patients with cardiovascular diseases have recently been explored to understand the mechanistic link between shape and pathophysiology. The construction of probabilistic atlases is based on statistical shape modeling (SSM) to assess key anatomic features for a given patient population. Such an approach is relevant to study the complex nature of the ascending thoracic aortic aneurysm (ATAA) as characterized by different patterns of aortic shapes and valve phenotypes. This study was carried out to develop an SSM of the dilated aorta with both bicuspid aortic valve (BAV) and tricuspid aortic valve (TAV), and then assess the computational hemodynamic of virtual models obtained by the deformation of the mean template for specific shape boundaries (i.e., ±1.5 standard deviation, σ). Simulations demonstrated remarkable changes in the velocity streamlines, blood pressure, and fluid shear stress with the principal shape modes such as the aortic size (Mode 1), vessel tortuosity (Mode 2), and aortic valve morphologies (Mode 3). The atlas-based disease assessment can represent a powerful tool to reveal important insights on ATAA-derived hemodynamic, especially for aneurysms which are considered to have borderline anatomies, and thus challenging decision-making. The utilization of SSMs for creating probabilistic patient cohorts can facilitate the understanding of the heterogenous nature of the dilated ascending aorta.