PowerPoint Presentation

Prof. Anthony Petrella

Musculoskeletal Modeling &The Importance of ValidationMEGN 536 Computational BiomechanicsMusculoskeletal ModelingYouve 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

MSM ApplicationsOrthopaedic Co #1 humeral fracture fixationOrthopaedic Co #2 compare different knee designs for kinematic patterns, muscle forces, and load transferMines projects in Center for Biomechanics and Rehabilitation Research

MSM in research & industryA broad topic of active research/evolutionCould teach a whole class on it (MEGN 535 in Spring)Validation a critical issue for future of MSMIntroduction to MSM ValidationMSM taking an increasingly central role in many ergonomics, design, clinical applicationsNASA digital astronaut (http://spaceflightsystems.grc.nasa.gov/SOPO/ICHO/HRP/DA/)Automotive ergonomics (Rasmussen et al., J Biomech, 2010)Orthopaedic designClinical guidance (Bohme et al., 2012)

Growing interest in personalized medicineMSM Consortium under IMAGSubject-specific simulation in literatureOrthopaedic companies offer variouspersonalized joint replacement technologies

How good is good enough?Musculoskeletal modeling

How good is good enough?

It Depends.Musculoskeletal modeling

How good is good enough? It depends.Can we believe model predictions, and can we use them to drive decisions that affect health?

Model influenceon decisionsConsequences of decisionsAdapted from (Mulugeta, 2012)MSM Consortium Mtgother factors driving,no/low risk of harmmodel driving, wrongchoices create harm

software can hurt peopleHow to know model is good?Verification & validation (V&V)Uncertainty quantification (UQ)Quality/version control (of each model) importantFormally developed software relatively youngSIMM early 1990sAnyBody early 2000sLifeModeler early 2000sOpenSim later 2000sStill learning best strategies andmethods for V&V et al.

TerminologyVerification testing code to ensure governing equations are implemented correctly and solved accuratelyValidation - the process of determining the degree to which a model is an accurate representation of the real world based on the intended uses of the model (AIAA, 1998)Direct gold standardIndirect use of surrogate metricsTrend parametric variation, confirm validity of what if scenariosValidation hierarchy test constituent parts of complex model

Uncertainty Quantification for relevant outcome metrics, and which inputs important?Version Control of individual modelsMSM Validation Somewhat UniqueModel DevelopmentCompany V&V (limited)Model Repository (AMMR)Community Validation, UQ, Version TrackingImprovementsAMMR = AnyBodyManagedModel RepositoryTypically done by developerMSM more challenging (vs. FE, CFD)Analytical solutions are rare, need experimentsExperiments laborious, difficult, introduce errorLine between V&V blurs

All MSM software vendors qualify codeModules/algorithms, system tests for interactions, models

When model influence + consequences Verification manual greater confidence?

Verification/AK/ Complete verification is impossible; only partial verification is possible (only certain parts of the MBM code). And only certain (restricted) input data sets can be used (for example those which can be solved analytically). And example is Sullivan (2007).

This is in contrast to models of certain medical devices (e.g. Pacemakers). I have the impression that for some of those a complete formal verification has been done /AK/11Direct Validation: In-vivo joint forces(Thielen et al., 2009)

(orthoload.com)12(de Jong and Meijer, 2006)

Direct (Pedal Forces), Indirect (EMG)Model13(Rasmussen et al., 2009)(Wilke et al., 1999)(In)direct and Trend Validation: In-vivo Pressure

Calibration14Direct and Trend Validation: ForceReaction forces at L1-L2Enhanced: interseg muscles, ligaments

(Han et al., 2012)

(orthoload.com)Direct and Trend Validation: Seat Shear Force

(Olesen, 2009)

16(Bergmann, 2009)Peak GH force = 863 NPeak GH force = 850 N

Model

Direct Validation: 45 Abduction, GH Force(Nolte et al., 2008;Dubowsky et al., 2008)Experiment

(orthoload.com)17

Direct Validation: 45 Abduction, GH Force

(orthoload.com)

WITH 2kg weight w/o 2kg18GH Lessons Learned

(Kunze, 2012)Closing the Loop: GH Improvements

Direct Validation: Knee ForcesGrand Challenge, In Vivo Knee Loads

(Andersen et al., 2011)Subject-Specific Scaling: Model OnlyHip center identified withRegression equation using pelvic landmarksCT scan register hip center to landmarks / markersGait, stair descent no difference in forceSit to stand CT significantly lower peak

( Andersen et al., 2012) Regression equationCT scan of pelvisSubject-Specific Scaling TLEMsafeNew complete and consistent musculoskeletal model including:Muscle LOAs, moment arms, and joint geometry based on cadaverBone surfaces and muscle volumes segmented from CT and MRIScalable to subject-specific models using MRI data, e.g., bone morphing and muscle volumes

(Carbone et al., ISB2013; www.tlemsafe.eu)

Bullet 3 the more specific geometry one has, the easier it will be to scale whole data set and get accurate representation of muscle attachments easier to work with a model like this23Validation HierarchyComplexity of high level model makes validation challengingConstituent parts can be(must be?) validated to add confidenceInput data (mocap, GRF, EMG) a sub system requiring validation

Benchmarks / standards can aid in validation of lower level system featuresMuscle benchmark data(Millard et al., 2012)

(Lund et al., 2012)Direct Sub-model Validation: Foot Contact

Uncertainty QuantificationNeed to understand sensitivity of outcomes to inputsNeed to understand uncertainty in the inputsCan determine uncertainty in the outcomes

Uncertainty QuantificationNeed to understand sensitivity of outcomes to inputsNeed to understand uncertainty in the inputsCan determine uncertainty in the outcomes

Probabilistic analysis

Validation CommentsAll validation examples experimental data vs. single modelDifferent versions, options, anatomical data setsHighlight credibility of model repository and software designBUT new models require new validation, UQ, version control

Auto-validation of standard (repository) models withvalidation report should be a goalEasier to select best model for appBetter insight to details momentarms, muscle parameters, etc.Greater number of input casesValidation engine could facilitatecommunity contribution

Gastroc moment arm vs. expsConclusionsMSM maturing, the software works

Many strong validation studies, butRelevant for single model onlyNew models require new validation

Some standards / benchmarks may be usefulVerification: standards?, published verification manual Validation: benchmarks, auto-validation for repository models, and validation engine for community contributionsUQ: probabilistic methods common, standards?Version: end users probably not used to this

Subject-specific: generally, detail = different, better?

How do I validatemy model?