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J Orthop Res. 2012 Apr;30(4):503-13. doi: 10.1002/jor.22023. Epub 2011 Dec 12.

Grand challenge competition to predict in vivo knee loads.

Author information

1
Department of Mechanical & Aerospace Engineering, University of Florida, Gainesville, Florida, USA. fregly@ufl.edu

Abstract

Impairment of the human neuromusculoskeletal system can lead to significant mobility limitations and decreased quality of life. Computational models that accurately represent the musculoskeletal systems of individual patients could be used to explore different treatment options and optimize clinical outcome. The most significant barrier to model-based treatment design is validation of model-based estimates of in vivo contact and muscle forces. This paper introduces an annual "Grand Challenge Competition to Predict In Vivo Knee Loads" based on a series of comprehensive publicly available in vivo data sets for evaluating musculoskeletal model predictions of contact and muscle forces in the knee. The data sets come from patients implanted with force-measuring tibial prostheses. Following a historical review of musculoskeletal modeling methods used for estimating knee muscle and contact forces, we describe the first two data sets used for the first two competitions and summarize four subsequent data sets to be used for future competitions. These data sets include tibial contact force, video motion, ground reaction, muscle EMG, muscle strength, static and dynamic imaging, and implant geometry data. Competition participants create musculoskeletal models to predict tibial contact forces without having access to the corresponding in vivo measurements. These blinded predictions provide an unbiased evaluation of the capabilities and limitations of musculoskeletal modeling methods. The paper concludes with a discussion of how these unique data sets can be used by the musculoskeletal modeling research community to improve the estimation of in vivo muscle and contact forces and ultimately to help make musculoskeletal models clinically useful.

PMID:
22161745
PMCID:
PMC4067494
DOI:
10.1002/jor.22023
[Indexed for MEDLINE]
Free PMC Article
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