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

• 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
• 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

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，尤其是在老年人和老年人中。 社会经济地位低下，他们对国家的整体健康状况造成了很大的影响。骨骼疾病是 通过探索患者的病史和体格检查，以及实验室检查，骨 活组织检查和影像学检查骨成像测试提供了一种非侵入性的方式来检查骨结构。然而，在这方面， 成像数据通常被定性地或依赖于操作者地评估 测量.这些定量测量不够灵敏，无法检测骨骼的细微变化 与早期疾病进展相关的质量。我们建议开发高性能，多模式， 和自动化3D骨骼表征工具，可通过Web浏览器访问。广泛 的研究人员和临床医生可以利用这些工具获得高通量，可重复的生物标志物， 统计敏感性研究。该系统将自动分割骨骼和软骨并进行量化 来自感兴趣区域的生物标志物。所提出的系统将具有上级高通量能力 它将为研究人员提供最先进的算法， 没有编程能力。除了为研究界提供强大的资源外，我们还将 将这一完整、精简的分析解决方案商业化，提供按图像处理收费的在线服务 的服务.我们的系统将通过证明我们可以在临床前检测骨骼退化来验证， 研究，这可能会导致新的临床试验的新的治疗和诊断方法， 人类我们将测试该系统可以自动识别膝关节图像中骨关节炎的假设 从骨关节炎倡议数据库和微计算机断层扫描图像区分血友病。 拟议项目的最终目标是导致更好的预防战略和改善进展 监测骨关节炎和相关疾病。