DC-AIDE - Dedicated Clinical Artificial Intelligence Deployment Equipment

• Subject Area: Medicine
• Term: Funded in 2023
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 512819079

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.

• DFG Programme: Major Research Instrumentation
• Major Instrumentation: DC-AIDE - Dedizierte klinische Ausrüstung für den Einsatz künstlicher Intelligenz
• Instrumentation Group: 7000 Datenverarbeitungsanlagen, zentrale Rechenanlagen
• Applicant Institution: Friedrich-Alexander-Universität Erlangen-Nürnberg
• Leader: Professor Dr. Bernhard Kainz