Quantitative, Image-Based Osteoarthritis Biomarkers Software Resubmission

基于图像的定量骨关节炎生物标志物软件重新提交

基本信息

批准号：
10207857
负责人：
Matthew McCormick
金额：
$ 45万
依托单位：
KITWARE, INC.
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-01 至 2022-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10207857
关键词：
3-Dimensional Adoption Algorithmic Analysis Algorithms Architecture Biological Markers Biopsy Bone Diseases Bone structure Cartilage Centers for Disease Control and Prevention (U.S.)Clinical Research Clinical Trials Clinical assessments Communities Computer software Data Data Set Database Management Systems Databases Degenerative polyarthritis Dependence Detection Deterioration Development Diabetes Mellitus Diagnosis Disease Disease Progression Documentation Dual-Energy X-Ray Absorptiometry Elderly Ensure Event Fees Geometry Goals Health Health Status Hemophilia A Human Image Image Analysis Individual Internet Knee Laboratories Laboratory Research Lead Lesion Magnetic Resonance Imaging Manuals Measurement Medical History Medical Imaging Methods Monitor Musculoskeletal Musculoskeletal Diseases Obesity Online Systems Osteoporosis Pathology Patients Performance Persons Phase Physical Examination Population Prevention strategy Process Quality of life Reporting Reproducibility Research Research Personnel Resources Rheumatism Rodent Roentgen Rays Scientist Services Signal Transduction Software Tools Statistical sensitivity System Testing Texture Thick Three-Dimensional Imaging Tissues Training United States Validation Variant Visual Acuity Work X-Ray Computed Tomography aging population algorithm development arthropathies automated segmentation base bone bone imaging bone quality computational pipelines computerized data processing cortical bone decision research design effective therapy graphical user interface image processing imaging modality improved insight interest interoperability large datasets low socioeconomic status microCT mouse model multimodality noninvasive diagnosis novel diagnostics novel therapeutics open data open source outreach pre-clinical research preclinical study quantitative imaging research study skeletal skeletal tissue socioeconomics spatiotemporal substantia spongiosa success symposium tool

项目摘要

PROJECT SUMMARY Musculoskeletal diseases are common in the United States, especially among the elderly and individuals of low socioeconomic status, and they take a large toll on the Nation's overall health status. Bone disorders are diagnosed by exploring a patient's medical history and by physical exam, alongside laboratory tests, bone biopsies, and imaging tests. Bone imaging tests provide a non-invasive way to examine at bone structure. However, imaging data is often evaluated qualitatively or with operator dependence as opposed to automated or quantitative measurements. These quantitative measurements are not sensitive enough to detect subtle variations in bone quality associated with early disease progression. We propose the development of high performance, multimodal, and automated 3D bone characterization tools, which are accessible through a web browser. A broad range of researchers and clinicians can leverage these tools to obtain high-throughput, reproducible biomarkers for statistically sensitive research studies. The system will automatically segment bone and cartilage and quantify biomarkers from the regions of interest. The proposed system will have superior high-throughput capabilities over existing bone image analysis suites, and it will provide access to state-of-the-art algorithms for researchers without programming abilities. In addition to providing a powerful resource to the research community, we will commercialize this complete, streamlined analytical solution by offering it as an online fee-per-image processing service. Our system will be validated by demonstrating that we can detect skeletal deterioration in preclinical studies, which can potentially lead to new clinical trials for novel therapeutic and diagnostic approaches in humans. We will test the hypothesis that the system can automatically identify osteoarthritis in knee images from the Osteoarthritis Initiative database and differentiate hemophilia in micro-computed tomography images. The ultimate goal of the proposed project is to lead to better preventive strategies and improved progression monitoring of osteoarthritis and related diseases.

项目摘要肌肉骨骼疾病在美国很常见，尤其是在老年人和个人中社会经济地位低下，他们对美国的整体健康状况造成了巨大损失。骨骼障碍是通过探索患者的病史和身体检查，以及实验室检查，骨骼诊断活检和成像测试。骨成像测试提供了一种非侵入性检查骨结构的方法。然而，成像数据通常是定性评估的，或与操作员依赖性相比，而不是自动化或定量的测量。这些定量测量不足以检测骨骼的细微变化与早期疾病进展相关的质量。我们提出了高性能，多模式的发展和自动化的3D骨特征工具，可以通过Web浏览器访问。广泛的范围研究人员和临床医生可以利用这些工具来获得高通量，可再现的生物标志物的统计敏感的研究。该系统将自动分割骨头和软骨并进行量化来自感兴趣地区的生物标志物。拟议的系统将具有出色的高通量功能在现有的骨骼图像分析套件上，它将为研究人员提供最先进的算法没有编程能力。除了为研究社区提供强大的资源外，我们还将通过将其作为每图像处理的在线收费处理，将该完整的简化分析解决方案商业化服务。我们的系统将通过证明我们可以在临床前检测骨骼恶化来验证研究可能会导致新的治疗和诊断方法的新临床试验人类。我们将测试系统可以自动识别膝盖图像中骨关节炎的假设从骨关节炎的倡议数据库中，在微型计算层析成像图像中区分了血友病。拟议项目的最终目标是提出更好的预防策略并改善进展监测骨关节炎和相关疾病。