DC-AIDE - Dedicated Clinical Artificial Intelligence Deployment Equipment

深度学习已成为生物医学成像分析的关键技术，但由于苛刻的计算和数据管理要求，对于非专家来说使用它是复杂的。该项目将开发一个平台，旨在通过尖端的深度学习方法简化多模态生物医学成像和患者数据的大规模统计分析。拟议的基础设施将提供对最先进算法的访问，并定义一个标准化的数据基础设施，便于在异构环境中部署。我们的原型将提供一种有效的机制，用于共享预先训练的AI算法和高级分析工具。该平台面向生物医学研究界，将为科学家提供新颖、强大和经过验证的工具，以应对基于图像的疾病表型分析和预测建模等挑战。 最新的分析管道将被实施并打包到易于使用的工具箱中，这些工具箱可直接部署到临床工作流程中，允许提取成像生物标志物和定量测量。我们的方法基于三个基本原则：数据链接（跨系统），数据治理（维护患者隐私和法律的/道德合规性）和数据互操作性（使用公共API和开放标准）。为了实现这一目标，我们将建立在现有模型的基础上：数据将保留在一个安全的环境中，人工智能算法将在我们合作伙伴诊所的防火墙内对敏感的患者数据进行训练。将支持两种方法：一个安全的学习编排服务器，用于在我们的合作伙伴医院大学诊所埃尔兰根（UKER）内为安全数据飞地执行学习协调，以及允许在FAU的大学托管安全环境内进行模型开发的安全沙箱。就像在联邦学习范式中一样，大多数模型将通过我们的基础设施，而不是数据。我们将在FAU生物医学工程人工智能（AIBE）部门，UKER放射科和Regionales Rechenzentrum埃尔兰根（RRZE）的支持下，在这些环境中放置（和支持）基础设施，以提供所需的功能。 我们将联合收割机结合HL 7 FHIR标准中的多模态机器学习和数据链接功能与XNAT的PACS数据，结合其他开源工具，为成像和患者记录数据提供完整的解决方案。这使得所提出的解决方案具有高度的互操作性和可扩展性，可以与其他诊所集成，例如，医学信息学倡议这种方法将提供对PACS和诊所电子病历的安全和合规访问，以及前瞻性同意的研究数据集，这将使临床医生和科学家能够以最有效的方式对大型患者队列进行可重复的研究。

Deep learning has emerged as a key technology in biomedical imaging analytics, but it is complex to use for non-experts due to demanding computational and data management requirements. This project will develop a platform that aims to simplify large-scale statistical analysis of multi-modal biomedical imaging and patient data with cutting-edge deep learning methods. The proposed infrastructure will provide access to state-of-the-art algorithms and define a standardised data infrastructure with easy deployment in heterogeneous environments. Our prototype will provide an effective mechanism for sharing pre-trained AI algorithms and advanced analysis tools. The platform targets the biomedical research community and will equip scientists with novel, powerful and validated tools to tackle challenges such as image-based disease phenotyping and predictive modelling. Latest analysis pipelines will be implemented and packaged into easy-to-use toolboxes that are directly deployable into clinical workflows, allowing extraction of imaging biomarkers and quantitative measurements. Our approach is based on three fundamental principles: data linkage (across systems), data governance (maintaining patient privacy and legal/ethical compliance), and data interoperability (using public APls and open standards). To deliver this, we will build on an existing model: data will be retained within a secure environment, with Al algorithms brought in for training on sensitive patient data inside our partner clinic’s firewall. Two approaches will be supported: a secure learning orchestration server to perform learning coordination for secure data enclaves within our partner hospital University clinic Erlangen (UKER), and secure sandboxes allowing model development within a university-hosted secure environment at FAU. Like in a federated learning paradigm, mostly models will move through our infrastructure, not the data. We will place (and support) infrastructure within these environments, with the support of the Department Artificial Intelligence in Biomedical Engineering (AIBE) at FAU, the Radiology Department at UKER, and Regionales Rechenzentrum Erlangen (RRZE), to provide the capabilities required. We will combine the capabilities of multi-modal machine learning and data linkage within the HL7 FHIR standard with XNAT for PACS data to provide a complete solution for imaging and patient record data, combined with additional open-source tools. This makes the proposed solution highly interoperable and scalable to other clinics and will enable integration with, e.g., the Medical Informatics Initiative. This approach will provide secure and regulatory compliant access to the PACS and electronic patient records of clinics, in addition to prospectively consented research datasets, which will allow clinicians and scientists to conduct reproducible research in the most efficient way on large patient cohorts.