De-identification of medical imaging data: a comprehensive tool for ensuring patient privacy
Question How can researchers effectively de-identify sensitive medical imaging data while complying with legal frameworks to protect patient health information? Findings We developed an open-source tool that automates the de-identification of various medical imaging formats, enhancing the efficiency of de-identification processes. Clinical relevance This tool addresses the critical need for robust and user-friendly de-identification solutions in medical imaging, facilitating data exchange in research while safeguarding patient privacy.
Authors
- Jens Kleesiek (ORCID: https://orcid.org/0000-0001-8686-0682)
- Fabian Hörst (ORCID: https://orcid.org/0000-0002-8906-7644)
- Moritz Rempe (ORCID: https://orcid.org/0000-0002-7137-2624)
- Constantin Seibold (ORCID: https://orcid.org/0000-0001-6042-8437)
- Lukas Heine (ORCID: https://orcid.org/0009-0004-3405-0556)
Institutions
- TU Dortmund University (DE)
- Institut für Medizinische Informatik, Biometrie und Epidemiologie (DE)
- Essen University Hospital (DE)
- Kliniken Essen-Mitte (DE)
- Deutsches Konsortium für Translationale Krebsforschung (DE)
Publication Details
- Journal
- European Radiology
- Published
- 2025-06-07
- DOI
- https://doi.org/10.1007/s00330-025-11695-x
- Citations
- 8
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 19.85
Funders
- Pancreatic Cancer Action Network
- Deutsche Forschungsgemeinschaft