De-identification of medical imaging data: a comprehensive tool for ensuring patient privacy.
Authors
Affiliations (8)
Affiliations (8)
- Institute for AI in Medicine (IKIM), University Hospital Essen, Essen, Germany. [email protected].
- Cancer Research Center Cologne Essen (CCCE), University Medicine Essen, Essen, Germany. [email protected].
- Department of Physics of the Technical University Dortmund, Dortmund, Germany. [email protected].
- Institute for AI in Medicine (IKIM), University Hospital Essen, Essen, Germany.
- Cancer Research Center Cologne Essen (CCCE), University Medicine Essen, Essen, Germany.
- Department of Physics of the Technical University Dortmund, Dortmund, Germany.
- RACOON Study Group, Site Essen, Essen, Germany.
- German Cancer Consortium (DKTK), Partner Site Essen, Essen, Germany.
Abstract
Medical imaging data employed in research frequently comprises sensitive Protected Health Information (PHI) and Personal Identifiable Information (PII), which is subject to rigorous legal frameworks such as the General Data Protection Regulation (GDPR) or the Health Insurance Portability and Accountability Act (HIPAA). Consequently, these types of data must be de-identified prior to utilization, which presents a significant challenge for many researchers. Given the vast array of medical imaging data, it is necessary to employ a variety of de-identification techniques. To facilitate the de-identification process for medical imaging data, we have developed an open-source tool that can be used to de-identify Digital Imaging and Communications in Medicine (DICOM) magnetic resonance images, computer tomography images, whole slide images and magnetic resonance twix raw data. Furthermore, the implementation of a neural network enables the removal of text within the images. The proposed tool reaches comparable results to current state-of-the-art algorithms at reduced computational time (up to × 265). The tool also manages to fully de-identify image data of various types, such as Neuroimaging Informatics Technology Initiative (NIfTI) or Whole Slide Image (WSI-)DICOMS. The proposed tool automates an elaborate de-identification pipeline for multiple types of inputs, reducing the need for additional tools used for de-identification of imaging data. 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.