Finite element analysis is widely used to study spinal biomechanics and to compare surgical strategies under controlled loading conditions. By allowing variation in alignment, fixation, and implant design, these models provide insight into stress redistribution and motion changes that are difficult to isolate experimentally. This review examines spine surgery-focused finite element studies published between 2018 and 2024, with emphasis on interbody fusion techniques, adjacent segment mechanics, and implant-related stress behavior. Across lumbar fusion models, constructs incorporating anterior column support demonstrate lower posterior instrumentation stress than posterior-only approaches, with lateral lumbar interbody techniques showing reduced rod and screw stresses across multiple loading conditions compared with posterior lumbar interbody or posterolateral fusion constructs. In the cervical spine, comparisons of plated and zero-profile anterior cervical discectomy and fusion devices show smaller increases in adjacent-level motion and intradiscal pressure with zero-profile constructs, alongside higher localized stress at fixation interfaces. More recent studies apply finite element methods to implant optimization, alignment planning, and patient-specific modeling. Together, these findings suggest that finite element analysis is increasingly used to support surgical planning and implant design, with continued advances in validation and patient-specific simulation likely to strengthen its clinical relevance.

Spinal fusion remains a common surgical treatment for degenerative cervical spine pathology. By eliminating segmental motion, fusion alters spinal biomechanics and redistributes mechanical loads to adjacent levels. These changes contribute to adjacent segment degeneration (ASD). Motion-preserving spinal implants have been developed to address these limitations. Cervical disc arthroplasty (CDA) is the most widely used example. Such devices aim to maintain physiologic kinematics while preserving segmental stability. Their biomechanical behavior varies with implant design, material properties, and constraint characteristics. Previous research does not holistically compare fusion with motion-preserving treatments on the spine, resulting in an incomplete understanding of when motion-preserving devices should be considered in treatment over fusion constructs and which specific motion-preserving implants are most appropriate. This narrative review synthesizes experimental, computational, and clinical studies comparing rigid fusion constructs to motion-preserving technologies in the cervical spine. Emphasis is placed on segmental range of motion, load transmission, intradiscal pressure, facet joint forces, and adjacent-segment mechanics. By comparing effectiveness across motion-preserving treatments, alongside their effectiveness to fusion constructs, we found that CDA more closely preserves near-physiologic motion compared to fusion. Taken together, this review underscores the importance of biomechanics-informed implant design for guiding future innovation in spinal implant technologies.

Lumbar spine disorders often require surgical intervention using medical implants to stabilize or replace damaged structures. As the prevalence of these surgeries increases due to an aging population, rigorous preclinical evaluation is critical. This narrative review aims to summarize current testing methods, identify gaps in clinical translatability, and explore the role of emerging computational technologies. Mechanical testing protocols established by the American Society for Testing and Materials (ASTM) and the International Organization for Standardization (ISO) provide essential standardized data on structural integrity but fail to replicate the complex biological interactions of the human spine. Similarly, animal models offer insights into biological responses like osseointegration but are limited by quadrupedal biomechanics and anatomical differences. Recent advancements in Artificial Intelligence (AI) and Finite Element Analysis (FEA) enable rapid, patient-specific modeling and high-throughput screening, significantly reducing the time and cost of physical testing. Future innovations include 3D-printed personalized implants, bio-responsive materials, and genetically modified animal models to bridge existing translatability gaps. In conclusion, improving the clinical success of lumbar spine implants requires an integrated framework that combines mechanical, biological, and computational approaches. This interdisciplinary collaboration is vital for developing safer and more effective treatments for patients.