Joint Imaging Platform for Federated Clinical Data Analytics
Jonas Scherer, Marco Nolden, Jens Kleesiek, Jasmin Metzger, Klaus Kades, Verena Schneider, Michael Bach, Oliver Sedlaczek, Andreas Bucher, Thomas J. Vogl, Frank Grünwald, Jens‐Peter Kühn, Ralf‐Thorsten Hoffmann, Jörg Kotzerke, O Bethge, Lars Schimmöller, Gerald Antoch, Hans‐Wilhelm Müller, Andreas Daul, Konstantin Nikolaou, Christian la Fougère, Wolfgang G. Kunz, Michael Ingrisch, Balthasar Schachtner, Jens Ricke, Peter Bartenstein, Felix Nensa, Alexander Radbruch, Lale Umutlu, Michael Forsting, Robert Seifert, Ken Herrmann, Philipp Mayer, Hans‐Ulrich Kauczor, Tobias Penzkofer, Bernd Hamm, Winfried Brenner, Roman Kloeckner, Christoph Düber, Mathias Schreckenberger, Rickmer Braren, Georgios Kaissis, Marcus R. Makowski, Matthias Eiber, Andrei Gafita, Rupert Trager, Wolfgang Weber, Jakob Neubauer, Marco Reisert, Michael Bock, Fabian Bamberg, Jürgen Hennig, Philipp T. Meyer, Juri Ruf, Uwe Haberkorn, Stefan O. Schoenberg, Tristan Anselm Kuder, Peter Neher, Ralf Floca, Heinz‐Peter Schlemmer, Klaus Maier‐Hein
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.