Integrative Predictors of Temporomandibular Osteoarthritis

颞下颌骨关节炎的综合预测因子

基本信息

批准号：
10165688
负责人：
Lucia H Cevidanes
金额：
$ 50.32万
依托单位：
UNIVERSITY OF MICHIGAN AT ANN ARBOR
依托单位国家：
美国
项目类别：
财政年份：
2013
资助国家：
美国
起止时间：
2013-09-13 至 2024-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10165688
关键词：
3-Dimensional Age Architecture Arthritis Benchmarking Biological Biological Markers Blood Bone remodeling Bone structure Cancer Center Chronic Classification Clinical Clinical Markers Computer Vision Systems Computer software Computer-Assisted Diagnosis Country Custom Data Data Analyses Data Analytics Data Set Data Storage and Retrieval Database Management Systems Databases Decision Trees Degenerative polyarthritis Dental Development Diagnosis Disease Early Diagnosis Environment Fibrocartilages Future Gaussian model Goals Hand Health Health Sciences Image Image Analysis Individual Inflammation Mediators Inflammatory Internet Joints Lasso Longitudinal cohort Machine Learning Mandibular Condyle Mediator of activation protein Medicine Methods Michigan Mining Minnesota Modeling Molecular Morphology North Carolina Online Systems Oregon Outcome Pain Paper Patients Pattern Peer Review Performance Phenotype Process Prognosis Property Proteins Publishing Replacement Arthroplasty Resolution Risk Saliva School Dentistry Scientific Advances and Accomplishments Severities Slice Structure Study models Symptoms System Technology Temporomandibular Joint Temporomandibular joint osteoarthritis Testing Texas Three-Dimensional Imaging Training Trees Universities University of Texas M D Anderson Cancer Center Work X-Ray Computed Tomography analytical tool base bone cadherin 5 cartilage degradation clinical center clinical diagnostics cone-beam computed tomography craniofacial craniomaxillofacial data repository deep learning deep neural network design high dimensionality imaging biomarker improved innovation joint destruction machine learning algorithm neural network neural network architecture novel novel strategies open source predictive modeling prospective quantitative imaging random forest repository scale up screening serial imaging software repository statistical and machine learning statistics subchondral bone support vector machine tool

项目摘要

ABSTRACT This application proposes the development of efficient web-based data management, mining, and analytics, to integrate and analyze clinical, biological, and high dimensional imaging data from TMJ OA patients. Based on our published results, we hypothesize that patterns of condylar bone structure, clinical symptoms, and biological mediators are unrecognized indicators of the severity of progression of TMJ OA. Efficiently capturing, curating, managing, integrating and analyzing this data in a manner that maximizes its value and accessibility is critical for the scientific advances and benefits that such comprehensive TMJ OA patient information may enable. High dimensional databases are increasingly difficult to process using on-hand database management tools or traditional processing applications, creating a continuing demand for innovative approaches. Toward this end, the DCBIA at the Univ. of Michigan has partnered with the University of North Carolina, the University of Texas MD Anderson Cancer Center and Kitware Inc. Through high-dimensional quantitative characterization of individuals with TMJ OA, at molecular, clinical and imaging levels, we will identify phenotypes at risk for more severe prognosis, as well as targets for future therapies. The proposed web-based system, the Data Storage for Computation and Integration (DSCI), will remotely compute machine learning, image analysis, and advanced statistics from prospectively collected longitudinal data on patients with TMJ OA. Due to its ubiquitous design in the web, DSCI software installation will no longer be required. Our long-term goal is to create software and data repository for Osteoarthritis of the TMJ. Such repository requires maintaining the data in a distributed computational environment to allow contributions to the database from multi-clinical centers and to share trained models for TMJ classification. In years 4 and 5 of the proposed work, the dissemination and training of clinicians at the Schools of Dentistry at the University of North Carol, Univ. of Minnesota and Oregon Health Sciences will allow expansion of the proposed studies. In Aim 1, we will test state-of-the-art neural network structures to develop a combined software module that will include the most efficient and accurate neural network architecture and advanced statistics to mine imaging, clinical and biological TMJ OA markers identified at baseline. In Aim 2, we propose to develop novel data analytics tools, evaluating the performance of various machine learning and statistical predictive models, including customized- Gaussian Process Regression, extreme boosted trees, Multivariate Varying Coefficient Model, Lasso, Ridge and Elastic net, Random Forest, pdfCluster, decision tree, and support vector machine. Such automated solutions will leverage emerging computing technologies to determine risk indicators for OA progression in longitudinal cohorts of TMJ health and disease.

抽象的该应用程序建议开发高效的基于网络的数据管理、挖掘和分析，以整合和分析来自 TMJ OA 患者的临床、生物和高维成像数据。基于我们发表的结果，我们假设髁骨结构的模式、临床症状和生物介质是 TMJ OA 进展严重程度的尚未识别的指标。高效地以最大化其价值的方式捕获、整理、管理、集成和分析这些数据对于此类全面的 TMJ OA 患者的科学进步和益处而言，可及性至关重要信息可能会启用。高维数据库越来越难以使用现有的方法进行处理数据库管理工具或传统处理应用程序，创造了对创新的持续需求接近。为此，大学的 DCBIA。密歇根大学与北方大学合作卡罗莱纳州、德克萨斯大学 MD 安德森癌症中心和 Kitware Inc. 通过高维在分子、临床和影像水平上对患有 TMJ OA 的个体进行定量表征，我们将确定面临更严重预后风险的表型，以及未来治疗的目标。拟议的基于网络的系统，计算和集成数据存储（DSCI），将远程计算机机器学习、图像分析和前瞻性收集的纵向数据的高级统计 TMJ OA 患者的数据。由于其在网络中无处不在的设计，DSCI 软件安装将不再被要求。我们的长期目标是创建颞下颌关节骨关节炎的软件和数据存储库。这样的存储库需要在分布式计算环境中维护数据，以便为来自多临床中心的数据库并共享用于 TMJ 分类的训练模型。在第 4 年和第 5 年拟议的工作、北方大学牙科学院临床医生的传播和培训卡罗尔，大学。明尼苏达州和俄勒冈州健康科学部将允许扩大拟议的研究。在目标 1 中，我们将测试最先进的神经网络结构来开发一个组合软件模块，其中包括最高效、最准确的神经网络架构和先进的统计数据，适用于矿山成像、临床和基线时确定的生物 TMJ OA 标记。在目标 2 中，我们建议开发新颖的数据分析工具，评估各种机器学习和统计预测模型的性能，包括定制的高斯过程回归、极限提升树、多元变系数模型、Lasso、Ridge 和弹性网络、随机森林、pdfCluster、决策树和支持向量机。此类自动化解决方案将利用新兴计算技术来确定 OA 纵向进展的风险指标颞下颌关节健康和疾病队列。