Documenting the de-identification process of clinical and imaging data for AI for health imaging projects

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  • dc.contributor.author Kondylakis, Haridimos
  • dc.contributor.author Garcia Lezena, Teresa
  • dc.contributor.author Tsiknakis, Manolis
  • dc.date.accessioned 2024-09-12T06:59:25Z
  • dc.date.available 2024-09-12T06:59:25Z
  • dc.date.issued 2024
  • dc.description.abstract Artificial intelligence (AI) is revolutionizing the field of medical imaging, holding the potential to shift medicine from a reactive "sick-care" approach to a proactive focus on healthcare and prevention. The successful development of AI in this domain relies on access to large, comprehensive, and standardized real-world datasets that accurately represent diverse populations and diseases. However, images and data are sensitive, and as such, before using them in any way the data needs to be modified to protect the privacy of the patients. This paper explores the approaches in the domain of five EU projects working on the creation of ethically compliant and GDPR-regulated European medical imaging platforms, focused on cancer-related data. It presents the individual approaches to the de-identification of imaging data, and describes the problems and the solutions adopted in each case. Further, lessons learned are provided, enabling future projects to optimally handle the problem of data de-identification. CRITICAL RELEVANCE STATEMENT: This paper presents key approaches from five flagship EU projects for the de-identification of imaging and clinical data offering valuable insights and guidelines in the domain. KEY POINTS: ΑΙ models for health imaging require access to large amounts of data. Access to large imaging datasets requires an appropriate de-identification process. This paper provides de-identification guidelines from the AI for health imaging (AI4HI) projects.
  • dc.description.sponsorship This work has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement no. 826494 (PRIMAGE), no. 952172 (Chaimeleon), no. 952103 (EuCanImage), no. 952159 (ProCancer-I), and no. 952179 (INCISIVE).
  • dc.format.mimetype application/pdf
  • dc.identifier.citation Kondylakis H, Catalan R, Alabart SM, Barelle C, Bizopoulos P, Bobowicz M, et al. Documenting the de-identification process of clinical and imaging data for AI for health imaging projects. Insights Imaging. 2024 May 31;15(1):130. DOI: 10.1186/s13244-024-01711-x
  • dc.identifier.doi http://dx.doi.org/10.1186/s13244-024-01711-x
  • dc.identifier.issn 1869-4101
  • dc.identifier.uri http://hdl.handle.net/10230/61062
  • dc.language.iso eng
  • dc.publisher Springer
  • dc.relation.ispartof Insights Imaging. 2024 May 31;15(1):130
  • dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/952103
  • dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/952179
  • dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/952159
  • dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/952172
  • dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/826494
  • dc.rights © The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
  • dc.rights.accessRights info:eu-repo/semantics/openAccess
  • dc.rights.uri http://creativecommons.org/licenses/by/4.0/
  • dc.subject.keyword Data anonymization
  • dc.subject.keyword Radiological imaging
  • dc.subject.keyword Radiology
  • dc.title Documenting the de-identification process of clinical and imaging data for AI for health imaging projects
  • dc.type info:eu-repo/semantics/article
  • dc.type.version info:eu-repo/semantics/publishedVersion

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