SBIR Phase I: COVID-19 Imaging XNAT Suite (CIXS): An informatics platform for developing, validating, and deploying AI applications for COVID-19 imaging

SBIR 第一阶段：COVID-19 成像 XNAT Suite (CIXS)：用于开发、验证和部署用于 COVID-19 成像的 AI 应用程序的信息学平台

• 批准号: 2031520
• 负责人: Timothy Olsen
• 金额: $ 25.59万
• 依托单位: RADIOLOGICS, INC.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2031520&HistoricalAwards=false
• 关键词: SBIR; Phase; COVID; 19; Imaging

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will be to implement an artificial intelligence (AI) method to assess COVID-19 in medical images, including diagnosis of the disease, prediction of disease severity, and measurement of treatment response. Given the ongoing COVID-19 pandemic, the primary aim of this proposal is to support frontline clinicians and researchers in caring for patients and developing effective therapies. Imaging is a critical but still underdeveloped component of COVID-19 patient care. This project will advance the understanding of COVID-19 imaging practices, utilizing best practices for training, validating, and deploying artificial intelligence applications. In addition, the imaging AI platform developed for creating COVID-19 algorithms will be broadly useful for a wide range of other radiology applications. This Small Business Innovation Research (SBIR) Phase I project aims to implement a platform that is uniquely positioned to enable full cycle development, validation, deployment, and ongoing tuning of imaging AI algorithms to improve the care of COVID-19 patients. Technical tasks include: developing methods to aggregate clinical and imaging data from diverse hospital information systems, implementing services and user interfaces to conduct federated learning experiments securely across remote data and computational systems, and developing mechanisms to publish and subscribe to validated high quality AI models for deployment in clinical environments. The core platform is already operational in many large academic medical centers, so translation will be straightforward.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该小企业创新研究 (SBIR) 第一阶段项目的更广泛影响/商业潜力将是实施人工智能 (AI) 方法来评估医学图像中的 COVID-19，包括疾病诊断、疾病严重程度预测和治疗反应的测量。鉴于当前的 COVID-19 大流行，该提案的主要目的是支持一线临床医生和研究人员护理患者和开发有效的疗法。 影像学是 COVID-19 患者护理中一个重要但仍不发达的组成部分。该项目将利用培训、验证和部署人工智能应用程序的最佳实践，加深对 COVID-19 成像实践的理解。 此外，为创建 COVID-19 算法而开发的成像 AI 平台将广泛用于其他广泛的放射学应用。该小型企业创新研究 (SBIR) 第一阶段项目旨在实现一个独特的平台，能够实现成像 AI 算法的全周期开发、验证、部署和持续调整，以改善对 COVID-19 患者的护理。技术任务包括：开发方法来聚合来自不同医院信息系统的临床和影像数据，实施服务和用户界面以跨远程数据和计算系统安全地进行联合学习实验，以及开发发布和订阅经过验证的高质量人工智能模型的机制在临床环境中的部署。该核心平台已经在许多大型学术医疗中心投入使用，因此翻译将很简单。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。