The cervical spine poses many complex challenges that require complex solutions. Anterior cervical discectomy and fusion (ACDF) has been one such technique often employed to address such issues. In order to address the problems with ACDF and assess the modifications that have been made to the technique over time, finite element analyses (FEA) have proven to be an effective tool. The variations of cervical spine FEA models that have been produced over the past couple of decades, particularly more recent representations of more complex geometries, have not yet been identified and characterized in any literature. Our objective was to present material property models and cervical spine models for various simulation purposes. The outlining and refinement of the FEA process will yield more reliable outcomes and provide a stable basis for the modeling protocols of the cervical spine.

This study explores the integration of artificial intelligence (AI) and finite element analysis (FEA) in spine surgery, highlighting their complementary roles across preoperative planning, intraoperative execution, and postoperative outcome prediction. The synergy between AI and FEA is reshaping modern spine care by improving biomechanical modeling, enhancing surgical precision, and enabling personalized treatment strategies. In the preoperative phase, AI-augmented FEA supports the design of patient-specific surgical plans, optimizing implant placement and simulating mechanical responses under various loading conditions. Intraoperatively, AI enables real-time image-guided navigation, robotic assistance, and automated anatomical recognition, reducing the risk of surgical error. Postoperatively, predictive models built on FEA simulations and patient data assist in tracking recovery, forecasting complications, and informing rehabilitation protocols. Together, these technologies contribute to a data-driven paradigm shift toward precision spine surgery. As intelligent feedback systems, digital twins, and autonomous surgical platforms continue to evolve, AI–FEA integration is poised to play a transformative role in delivering safer, more efficient, and individualized spine care.

Study Design: This study compares four cervical endplate removal procedures, validated by finite element models.Purpose: To characterize the effect of biomechanical strength and increased contact area on the maximum von Mises stress, migration, and subsidence between the cancellous bone, endplate, and implanted cage.Overview of Literature: Anterior cervical discectomy and fusion (ACDF) has been widely used for treating patients with degenerative spondylosis. However, no direct correlations have been drawn that incorporate the impact of the contact area between the cage and the vertebra/endplate.Methods: Model 1 (M1) was an intact C2C6 model with a 0.5 mm endplate. In model 2 (M2), a cage was implanted after removal of the C4–C5 and C5–C6 discs with preservation of the osseous endplate. In model 3 (M3), 1 mm of the osseous endplate was removed at the upper endplate. Model 4 (M4) resembles M3, except that 3 mm of the osseous endplate was removed.Results: The range of motion (ROM) at C2C6 in the M2–M4 models was reduced by at least 9º compared to the M1 model. The von Mises stress results in the C2C3 and C3C4 interbody discs were significantly smaller in the M1 model and slightly increased in the M2–M3 and M3–M4 models. Migration and subsidence decreased from the M2–M3 model, whereas further endplate removal increased the migration and subsidence as shown in the transition from M3 to M4.Conclusions: The M3 model had the least subsidence and migration. The ROM was higher in the M3 model than the M2 and M4 models. Endplate preparation created small stress differences in the healthy intervertebral discs above the ACDF site. A 1 mm embedding depth created the best balance of mechanical strength and contact area, resulting in the most favorable stability of the construct.

Comprehending the biomechanical characteristics of the human lumbar spine is crucial for managing and preventing spinal disorders. Precise material properties derived from patient-specific CT scans are essential for simulations to accurately mimic real-life scenarios, which is invaluable in creating effective surgical plans. The integration of Finite Element Analysis (FEA) with Physics-Informed Neural Networks (PINNs) offers significant clinical benefits by automating lumbar spine segmentation and meshing. We developed a FEA model of the lumbar spine incorporating detailed anatomical and material properties derived from high-quality CT and MRI scans. The model includes vertebrae and intervertebral discs, segmented and meshed using advanced imaging and computational techniques. PINNs were implemented to integrate physical laws directly into the neural network training process, ensuring that the predictions of material properties adhered to the governing equations of mechanics. The model achieved an accuracy of 94.30% in predicting material properties such as Young's modulus (14.88 GPa for cortical bone and 1.23 MPa for intervertebral discs), Poisson's ratio (0.25 and 0.47, respectively), bulk modulus (9.87 GPa and 6.56 MPa, respectively), and shear modulus (5.96 GPa and 0.42 MPa, respectively). We developed a lumbar spine FEA model using anatomical and material properties from CT and MRI scans. Vertebrae and discs were segmented and meshed with advanced imaging techniques, while PINNs ensured material predictions followed mechanical laws. The integration of FEA and PINNs allows for accurate, automated prediction of material properties and mechanical behaviors of the lumbar spine, significantly reducing manual input and enhancing reliability. This approach ensures dependable biomechanical simulations and supports the development of personalized treatment plans and surgical strategies, ultimately improving clinical outcomes for spinal disorders. This method improves surgical planning and outcomes, contributing to better patient care and recovery in spinal disorders.

Objectives: Lumbar spinal stenosis (LSS) refers to a narrowing of the space within the spinal canal, which can occur at any level but is most common in the lumbar spine. Open laminectomy and minimally invasive laminectomy (MIL) procedures are the most common surgical gold standard techniques for treating LSS. This study aims to review clinical and biomechanical literature to draw comparisons between open laminectomy and various MIL techniques. The MIL variation comprises microendoscopic decompression laminotomy, unilateral partial hemilaminectomy, and microendoscopic laminectomy.Methods: A review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We reviewed 25 clinical, 6 finite element, and 3 cadaveric studies associated with treating LSS. We reviewed literature that discusses factors such as operation time, length of hospital stay, postoperative complications, reoperation rate, effect on elderly patients, patients’ satisfaction, and adjacent segment disease degeneration for the clinical studies, whereas the range of motion (ROM), von Mises stresses, and stability was compared in biomechanical studies.Results: MIL involves less bone and ligament removal, resulting in shorter hospital stays and lower reoperation and complication rates than open laminectomy. It improves the quality of health-related living standards and reduces postoperative pain. Biomechanical studies suggest that laminectomy and facetectomy increase annulus stress and ROM, leading to segmental instability.Conclusion: Although theoretically, MIL means less tissue injury, pain, and faster recovery in the short term, the long-term results depend on the adequacy of the decompression procedure and tend to be independent of MIL or open laminectomy.