SBIR Phase II: Building the digital twin of radiology operations

小型企业创新研究(SBIR)第二阶段项目的更广泛影响/商业潜力是提高医疗成像设备的利用率，并有可能增加民众获得医疗成像的机会。今天，医学成像设施使用昂贵的设备，但缺乏对操作数据和现代工具的访问，无法更有效地监控和使用它们。该公司正在构建一个放射学操作的数字孪生兄弟，以持续捕获成像操作，对其进行监控，提出优化建议，并对患者进行最佳调度。除了报告和调度功能外，这些模型还将允许在软件中预测干预措施，以便在没有真实世界实验的情况下对不同的情景进行评估和比较。反过来，更有效地使用扫描仪有望潜在地使患者群体受益，因为这将减少成像的等待时间，增加患者探视机会，缩短成像方案，减少镇静时间，减少和预测延迟，并最终改善患者体验。统一在数字孪生兄弟中的数据也将为放射科医生和研究人员开辟新的研究途径。拟议的项目旨在开发和测试支撑我们的数字孪生兄弟愿景的关键技术创新。该公司将开发一个通用架构，以协调来自多个来源的数据，包括来自调度系统和扫描仪本身的数据。该公司将开发和测试新的人工智能(AI)技术，以增强数据并解锁管理运营的基本描述符。这一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表征图像的内容，并使其能够被查询。该公司将评估联合学习技术，以便在不共享数据的情况下，在大型数据集上进行大规模学习，从而缓解数据隐私挑战。该公司将把先进技术融入智能推荐引擎中，该引擎将持续挖掘客户数据，以确定改进机会并提出干预建议。最后，该公司将开发和测试实施智能日程安排助手的关键构建块，该助手使用追溯数据和数字模拟来优化考试日程，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优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.