Joint Imaging Platform for Federated Clinical Data Analytics

Abstract

PURPOSE Image analysis is one of the most promising applications of artificial intelligence (AI) in health care, potentially improving prediction, diagnosis, and treatment of diseases. Although scientific advances in this area critically depend on the accessibility of large-volume and high-quality data, sharing data between institutions faces various ethical and legal constraints as well as organizational and technical obstacles. METHODS The Joint Imaging Platform (JIP) of the German Cancer Consortium (DKTK) addresses these issues by providing federated data analysis technology in a secure and compliant way. Using the JIP, medical image data remain in the originator institutions, but analysis and AI algorithms are shared and jointly used. Common standards and interfaces to local systems ensure permanent data sovereignty of participating institutions. RESULTS The JIP is established in the radiology and nuclear medicine departments of 10 university hospitals in Germany (DKTK partner sites). In multiple complementary use cases, we show that the platform fulfills all relevant requirements to serve as a foundation for multicenter medical imaging trials and research on large cohorts, including the harmonization and integration of data, interactive analysis, automatic analysis, federated machine learning, and extensibility and maintenance processes, which are elementary for the sustainability of such a platform. CONCLUSION The results demonstrate the feasibility of using the JIP as a federated data analytics platform in heterogeneous clinical information technology and software landscapes, solving an important bottleneck for the application of AI to large-scale clinical imaging data.

Rise of Clinical Studies in the Field of Machine Learning: A Review of Data Registered in ClinicalTrials.gov

“…The reason for this is that access to large amounts of data will be essential for the further development of the approaches in prospective clinical studies. An example of how this could work in view of strict data protection requirements is shown by the Joint Imaging Platform for Federated Clinical Data Analytics for the application of medical algorithms across study sites in the field of medical imaging [ 77 ].…”

Radiologische Diagnostik und Prognostik von COVID-19: Einsatz von künstlicher Intelligenz und Zusammenhang mit muskulo-skelettaler Bildgebung

“…Damit kann der lokale Datenschutz gewährleistet werden. RACOON setzt hierfür die Joint Imaging Platform (JIP) des Deutschen Krebsforschungszentrums ein [34]. Die dort für Tumorerkrankungen entwickelten Verfahren ermöglichen föderiertes Lernen und werden im Rahmen von RAOON auf COIVD-19 Bedürfnisse zugeschneidert [26].…”

Federated Networks for Distributed Analysis of Health Data

“…The latter is termed federated learning ( 12 ), and has particular relevance in healthcare by allowing the training of a shared global algorithm on distributed sets of sensitive health data which typically does not leave their home nodes. Examples of existing clinical applications of federated learning networks include the Federated Tumor Segmentation network of 30 healthcare institutes working to improve tumor boundary detection ( 13 ), the AI4VBH (AI for value based healthcare) project, focusing on improving patient pathways in cancer, coronary artery disease, stroke, and COVID-19 using federated learning across 12 NHS trusts (UK hospitals) ( 14 ), and the Kaapana project, working through the Joint Imaging Platform across 36 German university hospitals with a focus on radiological and radiotherapeutical imaging data analysis to enable compliant and standardized approaches to imaging analysis within large-scale multi-center studies ( 15 ). A deeper look at FHDNs through the examination of the Personal Health Train and Vantage6 implementations is described below.…”