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Ovine Lumbar Intervertebral Disc Degeneration Model Utilizing a Lateral Retroperitoneal Drill Bit Injury
Published on: May 25, 2017
Biomechanical models of lumbar spine degeneration: Methods, challenges, and clinical promise
Hamed Hani1, Gregory G Knapik1, Eric C Bourekas2
1Spine Research Institute, The Ohio State University, 1971 Neil Ave, Columbus, 43210, OH, USA.
Background:
Lumbar disc degeneration is a leading cause of disability worldwide and a major contributor to low back pain. Biomechanical models have emerged as powerful tools to investigate the structural and mechanical changes associated with disc degeneration, offering insights that are difficult to obtain in vivo.
Methods:
This narrative review examines the progression of biomechanical modeling efforts in lumbar disc degeneration over the past decade. We reviewed studies from 2013 to 2024, focusing on both finite element and musculoskeletal modeling approaches. Key developments in model personalization, tissue degeneration simulation, and validation techniques were analyzed to assess their contribution to understanding spinal mechanics in degenerative states.
Findings:
Recent advancements have enabled more accurate representations of intervertebral disc pathology, incorporating subject-specific imaging, detailed tissue behavior, and complex loading scenarios. While finite element models have improved in capturing disc-level mechanical changes, musculoskeletal models have advanced in simulating whole-body dynamics and compensatory mechanisms. However, gaps remain in integrating these approaches and validating models against in vivo measurements. Few studies have directly linked modeling outcomes with clinical decision-making or patient-specific interventions.
Interpretation:
Biomechanical models have significantly improved our understanding of lumbar disc degeneration and its mechanical consequences. Future efforts should prioritize multimodal validation, integration of imaging and motion capture data, and development of clinically actionable models. Advancing these tools may enable more personalized and predictive approaches to spine care and support novel therapeutic strategies.
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