Development of an AI/ML-ready knee ultrasound dataset in a population-based cohort

在基于人群的队列中开发支持 AI/ML 的膝盖超声数据集

• 批准号: 10591756
• 负责人: Amanda E Nelson
• 金额: $ 30.45万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-05 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10591756
• 关键词: Administrative Supplement; Affect; Age; Area; Assessment tool; Atlases; Attention; Award; Benchmarking; Biochemical; Biological Markers; Black race; Chronic; Clinic; Clinical; Clinical Research; Clinical Trials; Collaborations; Communities; County; Data; Data Analyses; Data Set; Degenerative polyarthritis; Development; Disease; Documentation; Enrollment; Ethical Issues; Explosion; FAIR principles; Funding; Future; Goals; Grant; Health; Hispanic; Image; Image Analysis; Implant; Institutes; Joints; Knee; Knee Osteoarthritis; Lead; Magnetic Resonance Imaging; Manuscripts; Mentors; Methodology; Methods; Mission; Modality; Musculoskeletal; National Institute of Arthritis and Musculoskeletal and Skin Diseases; Nature; North Carolina; Pain; Parents; Phase; Phenotype; Population; Population Study; Preparation; Privacy; Protocols documentation; Public Health; Publishing; Radiation; Readiness; Renaissance; Reproducibility; Research; Resource-limited setting; Resources; Risk Factors; Role; Scoring Method; Standardization; Subgroup; Technology; Time; Tissues; Ultrasonography; United States; Universities; Validation; Variant; Visit; Weight; Woman; Work; aged; base; clinical center; clinical practice; cohort; convolutional neural network; cost; cost effective; deep learning; disability; follow-up; graduate student; health equity; imaging genetics; imaging modality; improved; innovation; insight; knee pain; men; metallicity; neural network algorithm; novel; parent project; population based; precision medicine; process optimization; prospective; radiological imaging; repository; response; soft tissue; ultrasound; uptake; Administrative Supplement; Affect; Age; Area; Assessment tool; Atlases; Attention; Award; Benchmarking; Biochemical; Biological Markers; Black race; Chronic; Clinic; Clinical; Clinical Research; Clinical Trials; Collaborations; Communities; County; Data; Data Analyses; Data Set; Degenerative polyarthritis; Development; Disease; Documentation; Enrollment; Ethical Issues; Explosion; FAIR principles; Funding; Future; Goals; Grant; Health; Hispanic; Image; Image Analysis; Implant; Institutes; Joints; Knee; Knee Osteoarthritis; Lead; Magnetic Resonance Imaging; Manuscripts; Mentors; Methodology; Methods; Mission; Modality; Musculoskeletal; National Institute of Arthritis and Musculoskeletal and Skin Diseases; Nature; North Carolina; Pain; Parents; Phase; Phenotype; Population; Population Study; Preparation; Privacy; Protocols documentation; Public Health; Publishing; Radiation; Readiness; Renaissance; Reproducibility; Research; Resource-limited setting; Resources; Risk Factors; Role; Scoring Method; Standardization; Subgroup; Technology; Time; Tissues; Ultrasonography; United States; Universities; Validation; Variant; Visit; Weight; Woman; Work; aged; base; clinical center; clinical practice; cohort; convolutional neural network; cost; cost effective; deep learning; disability; follow-up; graduate student; health equity; imaging genetics; imaging modality; improved; innovation; insight; knee pain; men; metallicity; neural network algorithm; novel; parent project; population based; precision medicine; process optimization; prospective; radiological imaging; repository; response; soft tissue; ultrasound; uptake

SUMMARY Our long-term goal is to demonstrate the utility of ultrasound for OA (OsteoArthritis) assessment, standardize its acquisition and scoring, and promote increased uptake of ultrasound for use in clinical, research, and trial settings. This supplement will allow us to enhance the original proposal by providing additional resources to support AI/ML approaches utilizing the image data in addition to the semiquantitative scoring we initially proposed. Knee osteoarthritis (KOA) is highly prevalent and frequently debilitating. Development of potential treatments has been hampered by the heterogeneous nature of this common chronic condition, which is characterized by several subgroups, or phenotypes, with different underlying pathophysiological mechanisms. Imaging, genetics, biochemical biomarkers, and other features can be used to characterize phenotypes, but variations in data types can make it difficult to harmonize definitions. Ultrasound is a widely accessible, time- efficient, and cost-effective imaging modality that can provide detailed and reliable information for all joint tissues. Application of deep learning methodology to discover ultrasound features associated with pain and radiographic change in KOA is highly innovative and will be a major step forward for the field. We will leverage standardized ultrasound images from the diverse and inclusive population-based Johnston County Health Study (JoCoHS), the new enrollment phase of the 30-year Johnston County OA Project which includes Black, White, and Hispanic men and women aged 35-70. In Aim 1, we will apply deep learning methodology to understand the features in ultrasound images that are most associated with knee pain and with radiographic features of knee OA in this diverse group. Aim 2 will allow the process of optimization for full AI/ML readiness of these images, including annotation, documentation, formatting, and storage of these images according to FAIR principles. This supplement will enhance the parent study by allowing AI/ML analysis of the ultrasound images, beyond just the semi-quantitative scores, and represents a crucial step to determine the ultrasound features of greatest importance to pain and other aspects of OA. By developing and maintaining an AI/ML ready repository of standardized ultrasound images from this generalizable cohort, we can enhance the uptake of this modality and contribute to further study on its use in OA worldwide, including in low-resource settings and across populations.

概括 我们的长期目标是证明超声对OA（骨关节炎）评估的实用性，并标准化 它的获取和评分，并促进超声吸收用于临床，研究和试验 设置。这种补充剂将使我们能够通过提供其他资源来增强原始建议 支持AI/ML使用图像数据，除了半定量评分外，我们最初是 建议的。膝盖骨关节炎（KOA）高度普遍，经常使人衰弱。潜力的发展 这种常见慢性病的异质性质阻碍了治疗 以几个亚组或表型为特征，具有不同的基础病理生理机制。 成像，遗传学，生化生物标志物和其他特征可用于表征表型，但 数据类型的变化可能使得很难统一定义。超声是一个广泛访问，时间 - 高效且具有成本效益的成像方式，可以为所有联合提供详细且可靠的信息 组织。深度学习方法的应用来发现与疼痛和疼痛相关的超声特征 KOA的影像学变化具有很高的创新性，这将是该领域的重要一步。我们将利用 来自多元化和包容人群的约翰斯顿县健康的标准化超声图像 研究（Jocohs），这是30年的约翰斯顿县OA项目的新入学阶段，包括黑色 白人和西班牙裔男女35-70岁。在AIM 1中，我们将应用深度学习方法 了解超声图像中与膝盖疼痛和射线照相最相关的特征 这个多元化组的膝盖OA的特征。 AIM 2将允许对完整AI/ML准备的优化过程 这些图像，包括注释，文档，格式化和这些图像的存储 公平的原则。该补充将通过允许对超声分析的AI/ML分析来增强家长研究 图像不仅是半定量得分，而且代表了确定超声的关键步骤 对于疼痛和OA的其他方面最重要的特征。通过开发和维护AI/ML 可从此可推广的队列的标准化超声图像的现成存储库，我们可以增强吸收 这种方式有助于进一步研究其在全球OA中的使用，包括在低资源环境中 以及整个人群。