MEGN 536 – Computational Biomechanics

1. MEGN 536 – Computational Biomechanics Prof. Anthony Petrella Musculoskeletal Modeling &The Importance of Validation

2. Musculoskeletal Modeling • You’ve worked with a simple arm curl model in the AnyBody Modeling System (AMS) • If you have not… you should try it (ws8, 10/1) • A student version of AMS is also available on the class website for your use on project (if you wish) • Consider the example:C:\Program Files\AnyBody Technology\AnyBody.6.0\AMMR\Application\Beta\TKA-KneeBendDemo\TKA-KneeBendDemo.Main.any

3. MSM Applications • Orthopaedic Co #1 – humeral fracture fixation • Orthopaedic Co #2 – compare different knee designs for kinematic patterns, muscle forces, and load transfer • Mines projects in Center for Biomechanics and Rehabilitation Research • MSM in research & industry… • A broad topic of active research/evolution • Could teach a whole class on it (MEGN 535 in Spring) • Validation a critical issue for future of MSM

4. Introduction to MSM Validation • MSM taking an increasingly central role in many ergonomics, design, clinical applications • NASA digital astronaut (http://spaceflightsystems.grc.nasa.gov/SOPO/ICHO/HRP/DA/) • Automotive ergonomics (Rasmussen et al., J Biomech, 2010) • Orthopaedic design • Clinical guidance (Bohme et al., 2012) • Growing interest in personalized medicine • MSM Consortium under IMAG • Subject-specific simulation in literature • Orthopaedic companies offer variouspersonalized joint replacement technologies

5. Musculoskeletal modeling… …How good is good enough?

6. Musculoskeletal modeling… …How good is good enough? It Depends.

7. How good is good enough? It depends. • Can we believe model predictions, and can we use them to drive decisions that affect health? “software can hurt people” model driving, wrongchoices create harm Model influenceon decisions other factors driving,no/low risk of harm Consequences of decisions Adapted from (Mulugeta, 2012)MSM Consortium Mtg

8. How to know model is good? • Verification & validation (V&V) • Uncertainty quantification (UQ) • Quality/version control (of each model) important • Formally developed software relatively young • SIMM – early 1990’s • AnyBody – early 2000’s • LifeModeler – early 2000’s • OpenSim – later 2000’s • Still learning best strategies andmethods for V&V et al.

9. Terminology • Verification – testing codeto ensure governing equations are implemented correctly and solved accurately • Validation - the process of determining the degree to which a model is an accuraterepresentation of the real world from the perspective of the intended uses of the model (AIAA, 1998) • Direct – gold standard • Indirect – use of surrogate metrics • Trend – parametric variation, confirm validity of “what if” scenarios • Validation hierarchy – test constituent parts of complex model • Uncertainty Quantification – for relevant outcome metrics, and which inputs important? • Version Control – of individual models

10. MSM Validation Somewhat Unique • Model Development • Improvements… • Company V&V (limited) AMMR = AnyBody Managed Model Repository • Community Validation, UQ, Version Tracking • Model Repository (AMMR)

11. Verification • Typically done by developer • MSM more challenging (vs. FE, CFD) • Analytical solutions are rare, need experiments • Experiments laborious, difficult, introduce error • Line between V&V blurs • All MSM software vendors qualify code • Modules/algorithms, system tests for interactions, models • When model influence + consequences Verification manual  greater confidence?

12. Direct Validation: In-vivo joint forces (orthoload.com) (Thielenet al., 2009)

13. Direct (Pedal Forces), Indirect (EMG) Model (de Jong and Meijer, 2006)

14. (In)direct and Trend Validation: In-vivo Pressure Calibration (Rasmussen et al., 2009) (Wilke et al., 1999)

15. Direct and Trend Validation: Force • Reaction forces at L1-L2 • Enhanced: interseg muscles, ligaments (orthoload.com) (Han et al., 2012)

16. Direct and Trend Validation: Seat Shear Force (Olesen, 2009)

17. Direct Validation: 45° Abduction, GH Force Experiment Peak GH force = 863 N (Bergmann, 2009) Model Peak GH force = 850 N (Nolte et al., 2008;Dubowsky et al., 2008) (orthoload.com)

18. Direct Validation: 45° Abduction, GH Force WITH 2kg weight w/o 2kg (orthoload.com)

19. GH Lessons Learned (Kunze, 2012)

20. Closing the Loop: GH Improvements

21. Direct Validation: Knee Forces • Grand Challenge, In Vivo Knee Loads (Andersen et al., 2011)

22. Subject-Specific Scaling: Model Only • Hip center identified with… • Regression equation using pelvic landmarks • CT scan register hip center to landmarks / markers • Gait, stair descent  no difference in force • Sit to stand  CT significantly lower peak Regression equation CT scan of pelvis ( Andersen et al., 2012)

23. Subject-Specific Scaling– TLEMsafe New complete and consistentmusculoskeletal model including: • Muscle LOA’s, moment arms, and joint geometry based on cadaver • Bone surfaces and muscle volumes segmented from CT and MRI • Scalable to subject-specific models using MRI data, e.g., bone morphing and muscle volumes (Carbone et al., ISB2013; www.tlemsafe.eu)

24. Validation Hierarchy • Complexity of high level model makes validation challenging • Constituent parts can be(must be?) validated to add confidence • Input data (mocap, GRF, EMG) a sub system requiring validation • Benchmarks / standards can aid in validation of lower level system features • Muscle benchmark data(Millard et al., 2012) (Lund et al., 2012)

25. Direct Sub-model Validation: Foot Contact

26. Uncertainty Quantification • Need to understand sensitivity of outcomes to inputs • Need to understand uncertainty in the inputs • Can determine uncertainty in the outcomes

27. Uncertainty Quantification • Need to understand sensitivity of outcomes to inputs • Need to understand uncertainty in the inputs • Can determine uncertainty in the outcomes • Probabilistic analysis

28. Validation Comments • All validation examples experimental data vs. single model • Different versions, options, anatomical data sets • Highlight credibility of model repository and software design • BUT… new models require new validation, UQ, version control • Auto-validation of standard (repository) models withvalidation report should be a goal • Easier to select best model for app • Better insight to details… momentarms, muscle parameters, etc. • Greater number of input cases • “Validation engine” could facilitatecommunity contribution Gastroc moment arm vs. exp’s

29. How do I validatemy model…? Conclusions • MSM maturing, the software works • Many strong validation studies, but… • Relevant for single model only • New models require new validation • Some standards / benchmarks may be useful • Verification: standards?, published verification manual • Validation: benchmarks, auto-validation for repository models, and “validation engine” for community contributions • UQ: probabilistic methods common, standards? • Version: end users probably not used to this • Subject-specific: generally, detail = different, better?