Computational lumbar spine models: A literature review

Area of Science:

• Biomechanics
• Computational modeling
• Spine research

Background:

• Computational spine models are crucial for understanding spinal function, injury risk assessment, and injury prevention techniques.
• The application scope of these models has expanded, potentially exceeding their current capabilities.
• A thorough understanding of model components is essential to grasp their limitations and potential.

Purpose of the Study:

• To critically assess the characteristics of model elements in lumbar spine modeling.
• To evaluate existing studies and identify areas for future research and refinement in spine modeling methodologies.
• To examine both traditional and combined modeling approaches.

Main Methods:

• A systematic review of 155 selected studies on lumbar spine modeling.
• Analysis of Finite Element models and Musculoskeletal models.
• Assessment of geometric and loading simplifications, subject variability, and cohort representation.

Main Results:

• Most studies utilize either detailed Finite Element models or simpler Musculoskeletal models.
• Significant simplifications in geometry and loading limit model realism and validity.
• Many models fail to account for subject variability, creating knowledge gaps.
• Combining modeling approaches shows potential for enhanced accuracy.

Conclusions:

• Integrated models combining different types can address current limitations in spine knowledge.
• Developing frameworks for integrated models allows for the evaluation of larger populations.
• This approach can lead to more realistic biomechanical representations of the lumbar spine.