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: 10250562
• 负责人: Matthew McCormick
• 金额: $ 44.59万
• 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 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10250562
• 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; open source tool; 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.

项目总结