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This is the development site of Reproducibility in simulation­-based prediction of natural knee mechanics. The development efforts for the project is through a collaboration between Ahmet Erdemir (Cleveland Clinic), Jason Halloran (Cleveland State University), Peter Laz & Kevin Shelburne (University of Denver), Carl Imhauser (Hospital for Special Surgery) and Thor Besier (Auckland Bioengineering Institute). The project is currently funded by the National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health (Grant No. R01EB024573).

The specific aims are

  • To quantify the influence of variations in modeling and simulation workflows on the reproducibility of joint level predictions in computational knee biomechanics.
  • To quantify the influence of variations in modeling and simulation workflows on the reproducibility of tissue level predictions in computational knee biomechanics.

Infrastructure

Meetings

Outreach

Advisory Board

Conferences

  • Erdemir A, Besier TF, Imhauser CW, Laz P, Morrison T, Shelburne K, Halloran JP. A collaborative pathway to establish credible practice of modeling and simulation in knee biomechanics in conformance with community recommendations. 2018 IMAG Futures Meeting – Moving Forward with the MSM Consortium, March 21-22, 2018, Bethesda, MD. Abstract Poster

Reviews & Perspectives

  • Erdemir A, Hunter PJ, Holzapfel GA, Loew LM, Middleton J, Jacobs CR, Nithiarasu P, Löhner R, Wei G, Winkelstein BA, Barocas VH, Guilak F, Ku JP, Hicks JL, Delp SL, Sacks M, Weiss JA, Ateshian GA, Maas SA, McCulloch AD, Peng GCY. Perspectives on Sharing Models and Related Resources in Computational Biomechanics Research. J Biomech Eng. 2018 Feb 1;140(2). doi: 10.1115/1.4038768. PubMed

Community

Project Overview

Summary Documents

Workflow

workflow.png

Timeline

timeline.png

Notes:

  1. Each modeling & simulation phase will have data processing and comparative analysis stages. Items above on data and comparative analysis is for the whole project.

  2. Processes should be defined for deposition & dissemination of data, specifications, protocol deviations & deliverables.

  3. Grant narrative provides guidance for the overall project workflow.

Context of Use of Models

For target clinical and research use cases (see model reuse):

  • Prediction of tibiofemoral and patellofemoral joint kinematics-kinetics
  • Prediction of tissue load sharing, individual tissue loads, and stress-strain

Data Resources

Modeling & Simulation Phases

Overall Strategy to Document and Execute M&S Phases

Each modeling & simulation phase of the project will be staged:

  • Define primary and secondary deliverables (by group consensus)
  • Define earmarked data (by group consensus)
  • Define timeline (by group consensus)
  • Curate earmarked data (by Cleveland Clinic & University of Denver teams)

  • Prepare earmarked data (to bring it in a usable form for exchange) (by group consensus)
  • Prepare specifications (by individual teams); this may need to reflect availability of data in two different data groups
  • Submit specifications (by individual teams)
  • Review specifications (by group consensus for adequacy of detail)
  • Execute specifications (by individual teams)
  • Document protocol deviations (by individual teams)
  • Submit protocol deviations (by individual teams)
  • Review protocol deviations (by group consensus for justification and for adequacy of detail)
  • Submit deliverables (by individual teams)
  • Review deliverables (by group concensus for completeness)
  • Prepare package for comparative analysis (by Cleveland Clinic team)
  • Comparative analysis (by third-party)

Each individual step will require expected detail and guidance to accomplish that expected detail. Individual phases will likely have time overlap, i.e., while specifications are executed for one phase, they can be developed for the next phase.

Model Development Phase

Goal. (each modeling team) To develop two initial working models (one from Open Knee(s); one from Natural Knee Data) - starting with earmarked data and providing all deliverables.

Earmarked Data. Specimen-specific medial imaging datasets (MRIs, CTs), other specimen-specific anatomical information (probing), literature.

Deliverables. (for each model) working model simulating a sample scenario, intermediate and final virtual representations of model components, sample simulation results; documentation of modeling and simulation processes - specifications (prior to execution), protocol deviations (changes in specifications during and posterior to execution)

Details. See ModelDevelopment.

Model Calibration Phase

Goal. To develop two calibrated working models (one from Open Knee(s); one from Natural Knee Data) - starting with earmarked data and providing all deliverables.

Earmarked Data. Specimen-specific joint mechanics datasets (joint kinematics-kinetics during laxity testing), other specimen-specific anatomical information (probing data during mechanical testing), literature.

Deliverables. (for each model) calibrated working model simulating the same sample scenario of the previous phase, simulation results of the sample scenario; for each calibration stage - calibrated parameters (before and after), representation of loading cases selected from earmarked data for calibration, simulation results of loading cases used for calibration (before and after), calibration fit error (before and after), intermediate and final virtual representations of model components that are changed during calibration; representation of all loading cases of earmarked data, simulation results of all loading cases of earmarked data with calibrated model; documentation of modeling and simulation processes - specifications (prior to execution), protocol deviations (changes in specifications during and posterior to execution).

Details. See ModelCalibration.

Model Benchmarking Phase

Goal. To benchmark re-calibrated models (one from Open Knee(s); one from Natural Knee Data) - re-calibrating with explicit descriptions and unified processing of raw calibration data (being faitful to calibration workflow of the previous phase), evaluating against earmarked benchmarking data from experimental loading conditions, and providing all deliverables.

Earmarked Data. For re-calibration, re-processed calibration data (specimen-specific joint mechanics) with explicit data descriptors. For benchmarking, processed specimen-specific joint mechanics (combined loading of the specimen through flexion for Open Knee(s), laxity loading of ACL resected specimen for Natural Knee Data).

Deliverables. (for each model) re-calibrated and benchmarked working model simulating the same sample scenario of the previous phases, simulation results of the sample scenario; for re-calibration - re-calibrated parameters, calibration fit error, simulation results of all loading cases of re-calibration data; for benchmarking, benchmarking error against experimental data, simulation results of all loading cases of benchmarking data; intermediate and final virtual representations of model components that are changed; representation of all loading cases; documentation of modeling and simulation processes - specifications (prior to execution), protocol deviations (changes in specifications during and posterior to execution).

Details. See ModelBenchmarking.

Model Reuse Phase

Goal. To reuse re-calibrated and benchmarked models (one from Open Knee(s); one from Natural Knee Data) in three use cases - prescribe use-case relevant loading and boundary conditions, predict joint/tissue metrics of interest, and compare between teams, and providing all deliverables.

Earmarked Data. None.

Deliverables. (for each model) models representative of loading and boundary conditions of each use case, simulation results of each use case; intermediate and final virtual representations of model components that are changed; documentation of modeling and simulation processes - specifications (prior to execution), protocol deviations (changes in specifications during and posterior to execution).

Details. See ModelReuse.

Comparative Analysis Consolidation

See ComparativeAnalysis.

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