Automated Analysis of Surface Roughness and Waviness in Vertebral Bodies by CT: Implications for Device Design in Spine Surgery.

Objective: The interactions between interbody devices, corpectomy cages, and vertebral bodies are influenced by surface characteristics such as roughness and waviness, impacting fusion quality and patient outcomes. Understanding vertebral surface morphology is crucial for improving cage design and avoiding complications like subsidence, neural injury, and re-operation. This study aims to characterize the variability in vertebral morphology along the spine.

Methods: A retrospective analysis of a spinal CT scan database of 159 patients (mean age 59±17) from an open-source database, encompassing 1895 vertebral bodies, was performed. Vertebral morphology was analyzed using an automated pipeline and clustered based on roughness and waviness to identify unique morphologic subgroups.

Results: Mean surface roughness (MSR) was highest in the cervical, upper thoracic, and lower lumbar spine, while mean surface waviness (MSW) peaked in the lower lumbar spine. Clustering revealed heterogeneous subgroups below C6, while the upper cervical spine had more homogeneous morphology (high roughness, low waviness). MSR and MSW were negatively correlated with sagittal slope at C3-6 and C7-T1. MSR was positively correlated with sagittal slope at T2-T11 and L4-L5, while MSW was positively correlated from T12-L3.

Conclusions: This study presents an automated method to measure MSR and MSW of spinal vertebral bodies. These parameters could aid in simulating the interaction between interbody devices and vertebral bodies preoperatively to allow for optimization of device design to enhance osseous integration. Future work will explore the relationship between these measures and clinical outcomes, particularly fusion quality.