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.

该小型企业创新研究（SBIR）第二阶段项目的更广泛影响/商业潜力是提高医学成像设备的利用率，并可能增加人口获得医学成像的机会。今天，医疗成像设施使用昂贵的设备，但缺乏获得操作数据和现代工具来更有效地监测和使用它们。该公司正在构建一个放射学操作的数字孪生模型，以持续捕获成像操作，监控它们，提出优化建议，并优化患者的时间安排。除了报告和调度功能之外，这些模型还可以预测软件中的干预措施，以评估和比较不同的情景，而无需进行真实世界的实验。预期扫描仪的更有效使用反过来可能使患者群体受益，因为它将减少成像的等待时间，增加患者访问，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。数字孪生中统一的数据也将为放射科医生和研究人员开辟新的研究途径。拟议项目旨在开发和测试支撑我们数字孪生愿景的关键技术创新。该公司将开发一个通用架构，以协调多个来源的数据，包括调度系统和扫描仪本身。该公司将开发和测试新的人工智能（AI）技术，以增加数据并解锁基本描述符来管理运营。该解决方案将包括人工智能，以被动地从患者的检查中学习成像协议，并自动检测协议偏差。该公司还将使用人工智能来自动描述图像的内容，并使其能够被查询。该公司将评估联邦学习技术，以允许在大规模数据集上进行“边缘”学习，而无需共享数据，从而缓解数据隐私挑战。该公司将把先进的技术整合到一个智能推荐引擎中，该引擎将不断挖掘客户数据，以发现改进的机会并提出干预措施。最后，该公司将开发和测试用于实施智能调度助手的关键构建模块，该智能调度助手使用回顾性数据和数字模拟来优化考试时间，同时最大限度地提高设备利用率。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。