Osteoarthritis is a costly and widespread degenerative condition with no cure – more than one third of the population over age 65 will suffer from this disease. The goal of this research project is to develop a holistic understanding of the factors that contribute to osteoarthritis onset and progression. Knowledge resulting from this study will enable personalized osteoarthritis risk predictions and assist clinicians in developing custom treatment plans to best prevent or slow the disease on a patient-specific basis. An integrated education and outreach plan will build technical communication skills within engineering student populations in Idaho, while simultaneously improving computational literacy in trainee clinicians and encouraging local elementary and high school students to engage in science, technology, engineering and math.
This CAREER project, jointly managed by the Disability and Rehabilitation Engineering Program (DARE) and the Established Program to Stimulate Competitive Research (EPSCoR), aims to develop a computational framework with the potential to transform patient-specific diagnosis and treatment decisions about osteoarthritis. The results of this research will advance scientific understanding of the primary structural, biological, and mechanical predictors of osteoarthritis onset and progression. Preventing or reducing osteoarthritis progression would have significant impacts on patients and on the healthcare system. Currently, there is no holistic framework for understanding osteoarthritis disease mechanisms. To address this need, the PI will build an analytical, data-driven framework to determine how multivariate factors contribute to osteoarthritis risk. The research objectives of this proposal are to (1) develop automated algorithms for rapid generation of subject-specific finite element knee models from medical images, (2) develop a statistical shape-function model for real-time prediction of knee joint mechanics, (3) determine the primary structural, biological, and mechanical predictors of osteoarthritis progression, and (4) develop an interactive computational platform to predict the longitudinal progression of knee osteoarthritis. This work promises the potential for transformative subject-specific diagnosis and treatment and illustrates the insight researchers across many domains can gain by combining computational tools with high-volume data from large-scale databases or probabilistic and statistical analyses.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
