DOI
https://doi.org/10.25772/M80M-FY57
Defense Date
2016
Document Type
Dissertation
Degree Name
Doctor of Philosophy
Department
Biomedical Engineering
First Advisor
Jennifer S. Wayne
Abstract
Computational joint models provide insight into the biomechanical function of human joints. Through both deformable and rigid body modeling, the structure-function relationship governing joint behavior is better understood, and subsequently, knowledge regarding normal, diseased, and/or injured function is garnered. Given the utility of these computational models, it is imperative to supply them with appropriate inputs such that model function is representative of true joint function. In these models, Magnetic Resonance Imaging (MRI) or Computerized Tomography (CT) scans and literature inform the bony anatomy and mechanical properties of muscle and ligamentous tissues, respectively. In the case of the latter, literature reports a wide range of values or average values with large standard deviations due to the inability to measure the mechanical properties of soft tissues in vivo. This makes it difficult to determine which values within the published literature to assign to computational models, especially patient-specific models. Therefore, while the use of published literature serves as a reasonable first approach to set up a computational model, a means of improving the supplied input data was sought.
This work details the application of artificial neural networks (ANNs), specifically feedforward and radial basis function networks, to the optimization of ligament stiffnesses for the improved performance of pre- and post-operative, patient-specific foot/ankle computational models. ANNs are mathematical models that utilize learning rules to determine relationships between known sets of inputs and outputs. Using knowledge gained from these training data, the ANN may then predict outputs for similar, never‑before-seen inputs. Here, an optimal network of each ANN type was found, per mean square error and correlation data, and then both networks were used to predict optimal ligament stiffnesses corresponding to a single patient’s radiographic measurements. Both sets of predictions were ultimately supplied to the patient-specific computational models, and the resulting kinematics illustrated an improvement over the existing models that utilized literature-assigned stiffnesses. This research demonstrated that neural networks are a viable means to hone in on ligament stiffnesses for the overall objective of improving the predictive ability of a patient-specific computational model.
Rights
© The Author
Is Part Of
VCU University Archives
Is Part Of
VCU Theses and Dissertations
Date of Submission
12-16-2016