Joint Imaging Platform for Federated Clinical Data Analytics
| Autor: | Ken Herrmann, Stefan O. Schoenberg, Lale Umutlu, Hans-Ulrich Kauczor, Winfried Brenner, Michael Bock, Klaus Kades, Oliver Th Bethge, Jens Kleesiek, Alexander Radbruch, Christoph Düber, Ralf Floca, Jasmin Metzger, Ralf-Thorsten Hoffmann, Rupert Trager, Jonas Scherer, Thomas J Vogl, Jürgen Hennig, Philipp T. Meyer, Michael Ingrisch, Klaus H. Maier-Hein, Jakob Neubauer, Fabian Bamberg, Konstantin Nikolaou, Marco Reisert, Matthias Eiber, Philipp Mayer, Michael Bach, Marcus R. Makowski, Hans-Wilhelm Müller, Andrei Gafita, Jens-Peter Kühn, Bernd Hamm, Christian la Fougère, Juri Ruf, Heinz-Peter Schlemmer, Robert Seifert, Tristan Anselm Kuder, Verena Schneider, Marco Nolden, Rickmer Braren, Peter Neher, Gerald Antoch, Georgios Kaissis, Peter Bartenstein, Uwe Haberkorn, Oliver Sedlaczek, Tobias Penzkofer, Felix Nensa, Jörg Kotzerke, Andreas M Bucher, Wolfgang A. Weber, Frank Grünwald, Wolfgang G. Kunz, Mathias Schreckenberger, Michael Forsting, Lars Schimmöller, Jens Ricke, Balthasar Maria Schachtner, Roman Kloeckner, Andreas Daul |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Computer science business.industry Data Science MEDLINE Medizin General Medicine ORIGINAL REPORTS Data science 03 medical and health sciences 030104 developmental biology 0302 clinical medicine Text mining Artificial Intelligence 030220 oncology & carcinogenesis Germany Health care Data analysis Joint imaging Humans Applications of artificial intelligence business Radiology Delivery of Health Care |
| Zdroj: | JCO Clinical Cancer Informatics |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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