SBIR Phase II: Building the digital twin of radiology operations

SBIR 第二阶段：构建放射学操作的数字孪生

• 批准号: 2304514
• 负责人: Benoit Scherrer
• 金额: $ 97.97万
• 依托单位: QUANTIVLY, INC.
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-15 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2304514&HistoricalAwards=false
• 关键词: SBIR; Phase; II; Building; digital

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project is to improve the utilization of medical imaging equipment and potentially increase access to medical imaging for the population. Today, medical imaging facilities operate expensive equipment but lack access to operational data and modern tools to monitor and use them more efficiently. The company is building a digital twin of radiology operations to continuously capture imaging operations, monitor them, suggest optimizations, and optimally schedule patients. Beyond reporting and scheduling capabilities, the models will allow the prediction of interventions in software for the evaluation and comparison of different scenarios in-silico without real-world experimentation. The more efficient use of scanners is expected, in turn, to potentially benefit the patient population as it will reduce the wait time for imaging, increase patient access, shorten imaging protocols, reduce sedation duration, reduce and predict delays, and ultimately improve the patient experience. The data unified in the digital twin will also open new avenues of research for radiologists and researchers. The proposed project aims at developing and testing key technological innovations underpinning our digital twin vision. The company will develop a generic architecture to harmonize data across many sources, including from the scheduling system and the scanners themselves. The company will develop and test new artificial intelligence (AI) techniques to augment the data and unlock essential descriptors to manage operations. This solution will include AI to passively learn imaging protocols from the patients’ exams and automatically detect protocol deviations. The company will also use AI to automatically characterize the content of images and enable them to be queried. The company will evaluate federated learning techniques to allow learning “at the edge” on large datasets at scale, without sharing the data, alleviating data privacy challenges. The company will incorporate advances into a smart recommendation engine that continuously mines customer data to identify opportunities for improvement and proposes interventions. Finally, the company will develop and test key building blocks for implementing a smart scheduling assistant that uses retrospective data and digital simulations to optimally schedule exams while maximizing equipment utilization.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.

这项小型企业创新研究（SBIR）II期项目的更广泛的影响/商业潜力是改善医学成像设备的利用，并有可能增加对人群的医学成像的获取。如今，医学成像设施可以操作昂贵的设备，但缺乏使用操作数据和现代工具来更有效地监视和使用它们。该公司正在建立一个数字双放射学操作，以连续捕获成像操作，监视它们，提出优化并最佳地安排患者。除了报告和调度功能外，这些模型还将允许预测软件中的干预措施，以评估和比较不同的场景，而无需实际实验。预计扫描仪会更有效地利用扫描仪，以便减少成像的等待时间，增加患者的访问时间，缩短成像协议，减少镇静持续时间，减少和预测延迟，并最终改善患者体验。数字双胞胎统一的数据还将为放射科医生和研究人员开放新的研究途径。拟议的项目旨在开发和测试基于数字双胞胎愿景的关键技术创新。该公司将开发一种通用体系结构，以协调许多来源的数据，包括调度系统和扫描仪本身。该公司将开发和测试新的艺术智能（AI）技术，以增强数据并解锁基本描述符以管理运营。该解决方案将包括AI，以被动地从患者的考试中学习成像协议，并自动检测协议出发。该公司还将使用AI自动表征图像的内容并使它们被查询。该公司将评估联合学习技术，以便在大规模上“在边缘”进行大规模学习，而无需共享数据，从而减轻了数据隐私挑战。该公司将将进步纳入智能推荐引擎中，该引擎继续挖掘客户数据以确定改进和建议干预措施的机会。最后，该公司将开发和测试关键构建块，用于实施智能调度助手，该助手使用回顾性数据和数字仿真来最佳计划考试，同时最大程度地提高设备利用率。该奖项反映了NSF的法定任务，并且我们是否通过使用基金会的知识分子优点和更广泛的影响审查审查标准来通过评估来诚实地支持我们的支持。