Validation of a Patient-Specific Musculoskeletal Model for Lumbar Load Estimation Generated by an Automated Pipeline From Whole Body CT - Lerchl, Tanja; El Husseini, Malek; Bayat, Amirhossein; Sekuboyina, Anjany; Hermann, Luis; Nispel, Kati; Baum, Thomas; Löffler, Maximilian T.; Senner, Veit; Kirschke, Jan S. (Frontiers in Bioengineering and Biotechnology, 2022)
Background: Chronic back pain is a major health problems worldwide. Its causes can be manifold, although biomechanical factors leading to spinal degeneration are considered to be a central issue. Numerical biomechanical models can identify critical factors and, thus, help predict impending spinal degeneration. However, spinal biomechanics are subject to significant interindividual variation. Therefore, in order to achieve meaningful findings on potential pathologies, predictive models have to take into account individual characteristics. To make these highly individualized models suitable for clinical practice, the automation of data processing and modeling itself is inevitable. Methods: CT imaging data from two patients with non-degenerative spines was processed using an automated deep learning based segmentation pipeline. In a semi-automated process with little user interaction we generated patient-specific musculoskeletal models and simulated various static loading tasks. To validate the model, calculated vertebral loading and muscle forces were compared to in vivo data from literature. Finally, results from both models were compared to assess the potential of our process for interindividual analysis. Results: Calculated vertebral loads and muscle activation overall stood in close correlation with data from literature. Compression forces normalized to upright standing deviated by a maximum of 16 % for flexion and 33 % for lifting tasks. Interindividual comparison of compression as well as lateral and anterior-posterior shear forces could be linked plausibly to individual spinal alignment and bodyweight. Conclusion: We developed a method to generate patient-specific musculoskeletal models of the lumbar spine in an automated manner. The models were able to calculate loads of the lumbar spine for static activities with respect to individual biomechanical properties, such as spinal alignment, bodyweight distribution and ligament and muscle insertion points.