Evaluation of a surrogate contact model in force-dependent kinematic simulations of total knee replacement

Marco Antonio Marra, Michael S. Andersen, Michael Damsgaard, Bart F.J.M. Koopman, Dennis Janssen, Nico Verdonschot

Research output: Contribution to journalArticleAcademicpeer-review

3 Citations (Scopus)
2 Downloads (Pure)

Abstract

Knowing the forces in the human body is of great clinical interest and musculoskeletal (MS) models are the most commonly used tool to estimate them in vivo. Unfortunately, the process of computing muscle, joint contact, and ligament forces simultaneously is computationally highly demanding. The goal of this study was to develop a fast surrogate model of the tibiofemoral (TF) contact in a total knee replacement (TKR) model and apply it to force-dependent kinematic (FDK) simulations of activities of daily living (ADLs). Multiple domains were populated with sample points from the reference TKR contact model, based on reference simulations and design-of-experiments. Artificial neural networks (ANN) learned the relationship between TF pose and loads from the medial and lateral sides of the TKR implant. Normal and right-turn gait, rising-from-a-chair, and a squat were simulated using both surrogate and reference contact models. Compared to the reference contact model, the surrogate contact model predicted TF forces with a root-mean-square error (RMSE) lower than 10N and TF moments lower than 0.3N-m over all simulated activities. Secondary knee kinematics were predicted with RMSE lower than 0.2mm and 0.2 deg. Simulations that used the surrogate contact model ran on average three times faster than those using the reference model, allowing the simulation of a full gait cycle in 4.5 min. This modeling approach proved fast and accurate enough to perform extensive parametric analyses, such as simulating subject-specific variations and surgical-related factors in TKR.

Original languageEnglish
Article number081001
Number of pages10
JournalJournal of biomechanical engineering : Transactions of the ASME
Volume139
Issue number8
DOIs
Publication statusPublished - 1 Aug 2017

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Knee prostheses
Knee Replacement Arthroplasties
Biomechanical Phenomena
Kinematics
Gait
Activities of Daily Living
Human Body
Ligaments
Mean square error
Knee
Joints
Muscles
Design of experiments
Muscle
Neural networks

Keywords

  • Contact
  • Musculoskeletal model
  • Surrogate model
  • TKA
  • TKR

Cite this

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title = "Evaluation of a surrogate contact model in force-dependent kinematic simulations of total knee replacement",
abstract = "Knowing the forces in the human body is of great clinical interest and musculoskeletal (MS) models are the most commonly used tool to estimate them in vivo. Unfortunately, the process of computing muscle, joint contact, and ligament forces simultaneously is computationally highly demanding. The goal of this study was to develop a fast surrogate model of the tibiofemoral (TF) contact in a total knee replacement (TKR) model and apply it to force-dependent kinematic (FDK) simulations of activities of daily living (ADLs). Multiple domains were populated with sample points from the reference TKR contact model, based on reference simulations and design-of-experiments. Artificial neural networks (ANN) learned the relationship between TF pose and loads from the medial and lateral sides of the TKR implant. Normal and right-turn gait, rising-from-a-chair, and a squat were simulated using both surrogate and reference contact models. Compared to the reference contact model, the surrogate contact model predicted TF forces with a root-mean-square error (RMSE) lower than 10N and TF moments lower than 0.3N-m over all simulated activities. Secondary knee kinematics were predicted with RMSE lower than 0.2mm and 0.2 deg. Simulations that used the surrogate contact model ran on average three times faster than those using the reference model, allowing the simulation of a full gait cycle in 4.5 min. This modeling approach proved fast and accurate enough to perform extensive parametric analyses, such as simulating subject-specific variations and surgical-related factors in TKR.",
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Evaluation of a surrogate contact model in force-dependent kinematic simulations of total knee replacement. / Marra, Marco Antonio; Andersen, Michael S.; Damsgaard, Michael; Koopman, Bart F.J.M.; Janssen, Dennis; Verdonschot, Nico.

In: Journal of biomechanical engineering : Transactions of the ASME, Vol. 139, No. 8, 081001, 01.08.2017.

Research output: Contribution to journalArticleAcademicpeer-review

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AU - Marra, Marco Antonio

AU - Andersen, Michael S.

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AU - Janssen, Dennis

AU - Verdonschot, Nico

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