Musculoskeletal spine modeling in large patient cohorts: how morphological individualization affects lumbar load estimation - Lerchl, Tanja; Nispel, Kati; Bodden, Jannis; Sekuboyina, Anjany; El Husseini, Malek; Fritzsche, Christian; Senner, Veit; Kirschke, Jan S. (Frontiers in Bioengineering and Biotechnology, 2024)
Achieving the adequate level of detail is a crucial part in any modeling process. Thus, oversimplification of complex systems can lead to overestimation, underestimation and general bias of effects, while elaborate models run the risk of losing validity due to uncontrolled interaction of multiple influencing factors and error propagation. We used a validated pipeline for automated generation of multi body models of the trunk to create 279 models based on CT data from 93 patients to investigate how different degrees of individualization affect the observed effects of different morphological characteristics on lumbar loads. Specifically, individual parameters related to spinal morphology (thoracic kyphosis, lumbar lordosis, torso height), as well as torso weight and distribution, were fully or partly considered in the respective models according to their degree of individualization, and effect strengths of these parameters on spinal loading were compared between semi-and highly individualized models. T-SNE analysis was performed for overarching pattern recognition and multiple regression analyses to evaluate changes in occurring effects sizes and significance. We were able to identify significant effects (p < 0.05) of various morphological parameters on lumbar loads in models with different degrees of individualization. Torso weight and lumbar lordosis showed strongest effects on compression (β ≈ 0.9) and anteriorposterior shear forces (β ≈ 0.7) respectively. We could further show, that effect strengthsingle effect sizes of individual parameters tended to decrease, if more individual characteristics were included in the models. The induced variability due to model individualization could only partly be explained by simple morphological parameter. Our study shows, that model simplification can lead to an emphasis on individual effects, which needs to be critically assessed with regard to in vivo complexity. At the same time, we demonstrated, that individualized models representing 1 Lerchl et al.a population-based cohort are still able to identify relevant influences on spinal loading, while considering a variety of influencing factors and their interactions.