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

Fecha de publicación:

Autores de IIS La Fe

Participantes ajenos a IIS La Fe

  • Kondylakis, H
  • Catalan, R
  • Alabart, SM
  • Barelle, C
  • Bizopoulos, P
  • Bobowicz, M
  • Bona, J
  • Fotiadis, DI
  • Garcia, T
  • Jimenez-Pastor, A
  • Karatzanis, G
  • Lekadir, K
  • Kogut-Czarkowska, M
  • Lalas, A
  • Marias, K
  • Munuera, J
  • Nikiforaki, K
  • Pelissier, M
  • Prior, F
  • Rutherford, M
  • Saint-Aubert, L
  • Sakellariou, Z
  • Seymour, K
  • Trouillard, T
  • Votis, K
  • Tsiknakis, M

Grupos

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.

Datos de la publicación

ISSN/ISSNe:
1869-4101, 1869-4101

Insights into Imaging  SPRINGER WIEN

Tipo:
Article
Páginas:
-
PubMed:
38816658
Factor de Impacto:
1,253 SCImago ℠
Cuartil:
Q1 SCImago ℠

Citas Recibidas en Web of Science: 16

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Keywords

  • Data anonymization; Radiology; Radiological imaging

Proyectos y Estudios Clínicos

PRIMAGE. PREDICTIVE IN-SILICO MULTISCALE ANALYTICS TO SUPPORT CANCER PERSONALIZED DIAGNOSIS AND PROGNOSIS, EMPOWERED BY IMAGING BIOMARKERS.

Investigador Principal: LUIS MARTÍ BONMATÍ

826494 . COMISION EUROPEA . 2018

PROCANCER-I: AN AI PLATFORM INTEGRATING IMAGING DATA AND MODELS, SUPPORTING PRECISION CARE THROUGH PROSTATE CANCER’S CONTINUUM.

Investigador Principal: LUIS MARTÍ BONMATÍ

952159 . COMISION EUROPEA . 2020

CHAIMELEON. ACCELERATING THE LAB TO MARKET TRANSITION OF AI TOOLS FOR CANCER MANAGEMENT.

Investigador Principal: LUIS MARTÍ BONMATÍ

952172 . COMISION EUROPEA . 2020

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