CAREER: Computational modeling to predict subject-specific osteoarthritis risk and facilitate treatment

骨关节炎是一种昂贵且广泛的退行性疾病，无法治愈-超过三分之一的65岁以上的人口将患有这种疾病。本研究项目的目标是全面了解导致骨关节炎发病和进展的因素。从这项研究中获得的知识将使个性化的骨关节炎风险预测成为可能，并帮助临床医生制定定制的治疗计划，以最好地预防或减缓患者特定基础上的疾病。一项综合教育和推广计划将在爱达荷州的工程专业学生群体中建立技术沟通技能，同时提高实习临床医生的计算素养，并鼓励当地小学和高中学生从事科学，技术，工程和数学。由残疾和康复工程计划（DARE）和既定计划，以刺激竞争力的研究（EPSCoR）联合管理，旨在开发一个计算框架，有可能改变患者特定的诊断和治疗决定有关骨关节炎。这项研究的结果将促进对骨关节炎发病和进展的主要结构、生物学和力学预测因子的科学理解。预防或减少骨关节炎进展将对患者和医疗保健系统产生重大影响。 目前，还没有一个全面的框架来理解骨关节炎疾病的机制。为了满足这一需求，PI将建立一个分析性的数据驱动框架，以确定多变量因素如何影响骨关节炎风险。本提案的研究目标是：（1）开发自动化算法，用于从医学图像快速生成受试者特定的有限元膝关节模型;（2）开发统计形状-功能模型，用于实时预测膝关节力学;（3）确定骨关节炎进展的主要结构、生物学和力学预测因子，以及（4）开发一个交互式计算平台来预测膝骨关节炎的纵向进展。这项工作有望为特定学科的诊断和治疗带来变革性的潜力，并说明了跨许多领域的研究人员可以通过将计算工具与来自大规模数据库或概率和统计分析的大量数据相结合来获得的洞察力。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

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.