Validation of a Coordinate Optimization Approach for Assessment of In Vivo Intervertebral Kinematics in Patients With Adult Spinal Deformity and Healthy Older Adults

Abstract

Background

Spine kinematics assessment is crucial for understanding intervertebral joint motion, particularly in conditions like spinal deformity, which alters and reduces spinal motion. Estimating spine kinematics in vivo usually relies on kinematic constraints to reduce the degrees of freedom in musculoskeletal models, but they lack standardization and fail to generalize across populations. This study proposes a novel method utilizing coordinate optimization instead of kinematic constraints, aiming to improve the generalizability and accuracy of spine kinematics estimation across different populations and marker protocols.

Methods

This study used two retrospective datasets: 13 subjects with spinal deformities and 11 healthy individuals. Spine kinematics were estimated by minimizing errors between simulated and experimental marker positions and penalizing large intervertebral joint angles. 3D orientation and position errors against image-based ground truth vertebral orientations and positions and experimental marker positions were calculated and compared for eight different weight settings. The accuracy was further assessed using standard error of measurements (SEM) compared to kinematic constraint methods.

Results

The best-performing optimization settings resulted in average vertebral orientation errors of 5.1°, 3.2°, and 3.2° for axial rotation, lateral bending, and flexion-extension, respectively, and 3D position errors of 7.7 mm. These values reflect the average of vertebra-specific errors within each subject, further averaged across all subjects in the deformity dataset. Similarly, in the healthy dataset, average 3D marker errors remained below 1 cm, and SEM values remained below 1.3°.

Conclusions

The coordinate optimization method showed robust performance, achieving high accuracy in vertebral orientation and position (deformity) and marker tracking (healthy). This method consistently matched or surpassed state-of-the-art kinematic constraints methods while introducing generalizability across different populations and marker protocols.