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

摘要 我们的长期目标是证明超声波对骨关节炎(骨关节炎)评估的有效性，标准化 它的获取和评分，并促进在临床、研究和试验中使用的超声吸收增加 设置。这份补编将使我们能够通过提供额外资源来加强原来的建议 除了我们最初的半定量评分外，还支持利用图像数据的AI/ML方法 建议。膝骨性关节炎(KOA)是一种非常普遍且经常使人虚弱的疾病。潜力的开发 这种常见慢性疾病的异质性阻碍了治疗，这是 以几个亚群或表型为特征的，具有不同的潜在病理生理机制。 成像、遗传学、生化生物标记物和其他特征可以用来表征表型，但 数据类型的变化可能会使协调定义变得困难。超声波是一种随处可得的时间- 高效、经济实惠的成像方式，可为所有关节提供详细可靠的信息 纸巾。应用深度学习方法发现与疼痛和疼痛相关的超声特征 KOA的放射学改变具有很高的创新性，将是该领域向前迈出的重要一步。我们将利用 来自以约翰斯顿县健康中心为基础的多样化和包容性人群的标准化超声图像 研究(JoCoHS)，为期30年的约翰斯顿县OA项目的新注册阶段，其中包括Black， 白人和西班牙裔男性和女性年龄在35-70岁之间。在目标1中，我们将应用深度学习方法来 了解超声图像中与膝关节疼痛和X光检查最相关的特征 不同人群膝关节骨性关节炎的特点。AIM 2将允许针对完全AI/ML就绪的优化过程 ，包括这些图像的注释、文档、格式化和存储 公平原则。这一补充将通过允许对超声进行AI/ML分析来增强家长研究 图像，而不仅仅是半定量的分数，代表着确定超声的关键一步 对骨性关节炎的疼痛和其他方面最重要的特征。通过开发和维护AI/ML 从这个可推广的队列中获得标准化超声图像的现成存储库，我们可以增强对 并有助于进一步研究其在全世界开放式获取中的使用情况，包括在低资源环境下的使用情况 以及不同人群之间的。

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.