In a groundbreaking study recently published in the Journal of Biomechanics, researchers from the Bern University of Applied Sciences and University of Bern, Switzerland, have made significant progress in the field of biomechanical simulations related to adolescent idiopathic scoliosis (AIS). The study focused on the predictive muscle activity in patients with AIS using musculoskeletal (MSK) models and validated them with actual electromyography (EMG) measurements during various functional activities. This represents a crucial step towards enhancing the treatment and understanding of AIS.
Adolescent idiopathic scoliosis is a spinal condition characterized by an abnormal, three-dimensional curvature of the spine that arises during adolescent growth. The management of AIS often involves monitoring the spinal curvature’s progression and applying necessary interventions, which may include bracing or surgery. Muscle forces around the spine play a critical role in AIS development and progression, yet capturing these forces in vivo remains challenging.
To improve the accuracy of AIS treatment simulations, researchers led by Cedric C. Rauber and his co-authors have utilized OpenSim, an open-source software for biomechanical modeling. Their research, “Predicted vs. measured paraspinal muscle activity in adolescent idiopathic scoliosis patients: EMG validation of optimization-based musculoskeletal simulations,” showcases an approach combining biplanar radiographs, marker-based motion capture, ground reaction force, and EMG to provide a detailed understanding of spinal loading during movement.
Methodology and Observations
The team’s methodology is notable for its fully automated extraction of 3D spinal shapes from radiographs and its creation of geometrically personalized MSK models. In their study, they tracked two AIS patients, one with mild thoracolumbar AIS (21° Cobb angle) and another with moderate AIS (45° Cobb angle), through various activities including standing with weights, walking, running, and lifting objects.
The computational models predicted muscle activity for each patient, while EMG provided real-time, in vivo muscle activation data. The study’s core analyses focused on the root mean square error (RMSE) and cross-correlation (XCorr) between predicted and measured muscle activities.
The findings revealed that for mild AIS, especially during object lifting, the model’s predictions closely aligned with the EMG measurements (XCorr ≥ 0.97), highlighting the efficacy of the musculoskeletal models for milder deformities. However, as the severity of AIS and the complexity of the activities increased, particularly with walking and running, the correlations were less strong (XCorr as low as 0.51), indicating that static optimization alone is limited in its predictive capability.
Implications for Clinical Practice
The study holds significant implications for clinical practice. The high accuracy of muscle activity predictions in mild AIS cases during specific activities demonstrates that personalized computational models can be a powerful tool for planning interventions. However, the reduced accuracy in moderate AIS cases and during dynamic activities underscores the need for improved modeling techniques that can capture the complexity of spinal muscular interactions, particularly in more advanced AIS cases or during strenuous activities.
While the study represents an advancement in the field of biomechanical simulations for spinal conditions, the authors acknowledge the limitations of static optimization. These limitations highlight the need for further research into dynamic simulations that can better replicate the intricate behaviors of muscles during various movements.
Emerging Technologies and Future Directions
The potential for non-invasive treatment planning using technological innovations such as MSK models is vast. Future research might focus on incorporating machine learning algorithms that can refine predictive accuracy based on a wide range of movement data, or the development of dynamic simulation models that can adapt to the rapid changes in muscular forces during activities.
DOI and References
The research findings were published with the following DOI: 10.1016/j.jbiomech.2023.111922. This DOI provides a persistent digital identifier that facilitates access to the study’s digital location.
References
1. Rauber, C. et al. (2024). Predicted vs. measured paraspinal muscle activity in adolescent idiopathic scoliosis patients: EMG validation of optimization-based musculoskeletal simulations. Journal of Biomechanics, 111922. DOI: 10.1016/j.jbiomech.2023.111922
2. Stokes, I. A. F., et al. (2013). Biomechanical spinal growth modulation and progressive adolescent scoliosis – a test of the ‘vicious cycle’ pathogenetic hypothesis: Summary of an electronic focus group debate of the IBSE. Scoliosis, 1(16).
3. Cheung, J., et al. (2019). The application of musculoskeletal modeling to investigate gender difference in effect of functional electrical stimulation on energy expenditure during walking. Journal of NeuroEngineering and Rehabilitation, 16(1), 74.
4. Modenese, L., et al. (2013). Estimation of musculotendon parameters for scaled and subject-specific musculoskeletal models using an optimization technique. Journal of Biomechanics, 46(2), 381–386.
5. Viceconti, M., et al. (2006). Multiscale modeling of the skeletal system. Cambridge University Press.
Keywords
1. Adolescent idiopathic scoliosis
2. Musculoskeletal modeling
3. EMG validation
4. Spine biomechanics
5. AIS treatment simulation
Conclusion
The study by Rauber and colleagues is a testament to the innovative strides being made in the pursuit of more accurate treatment simulations for AIS. Their work emphasizes the need for continued refinement of computational models to ensure that patients with this common spinal condition receive the most effective and personalized care possible. The integration of technology and clinical expertise is paving the way for new horizons in non-invasive diagnostics and therapeutic planning for scoliotic deformities.