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Comparative validation of two patient-specific modelling pipelines for predicting knee joint forces during level
Domitille Princelle1, Giorgio Davico1, Marco Viceconti1
1Medical Technology Lab, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy; Department of Industrial Engineering, Alma Mater Studiorum - University of Bologna, Italy.
Journal of Biomechanics
|September 2, 2023
Summary
A new semi-automated pipeline using STAPLE and nmsBuilder rapidly creates personalized musculoskeletal models. These models show comparable joint contact forces to established methods, significantly reducing development time for clinical applications.
Area of Science:
- Biomechanics
- Medical Imaging
- Computational Modeling
Background:
- Personalized musculoskeletal models aid clinical decisions but face adoption barriers due to complex computational needs and long development times.
- Novel toolboxes like STAPLE aim to simplify and accelerate the creation of patient-specific lower limb models.
Purpose of the Study:
- To compare the accuracy of joint contact force predictions from musculoskeletal models developed using the established INSIGNEO pipeline versus a semi-automated pipeline combining STAPLE and nmsBuilder.
- To assess the time efficiency of the semi-automated pipeline for generating personalized musculoskeletal models.
Main Methods:
- Development of image-based musculoskeletal lower limb models using two distinct pipelines: the INSIGNEO pipeline and a semi-automated pipeline (STAPLE + nmsBuilder).
- Comparison of predicted joint kinematics, kinetics, and total knee joint contact forces between the two pipelines.
- Validation of model accuracy against experimental implant data.
Main Results:
- Both pipelines yielded similar total knee joint contact force profiles and average values, with a moderately high agreement with experimental data.
- The STAPLE-based pipeline significantly reduced model development time (60 minutes) compared to the INSIGNEO pipeline (160 minutes).
- Statistically significant differences were observed between the pipelines despite overall similarity in contact force predictions.
Conclusions:
- The semi-automated STAPLE and nmsBuilder pipeline offers a time-efficient alternative for creating personalized musculoskeletal models.
- The generated models demonstrate comparable accuracy in predicting knee joint contact forces, supporting their potential clinical utility.
- Reduced development time is crucial for integrating personalized musculoskeletal modeling into time-sensitive clinical practice.
