INNOVATIVE APPLICATIONS OF ASSESSMENT AND ASSURANCE OF DATA AND SYNTHETIC DATA FOR REGULATORY DECISION SUPPORT

Informazioni relative al progetto

INSAFEDARE

ID dell’accordo di sovvenzione: 101095661

DOI

10.3030/101095661

Data della firma CE 8 Novembre 2023

Data di avvio 1 Novembre 2023

Data di completamento 31 Ottobre 2026

Finanziato da

• Health

Costo totale

€ 2 855 645,00

Contributo UE

€ 2 855 645,00

Coordinato da

COMMISSARIAT A L ENERGIE ATOMIQUE ET AUX ENERGIES ALTERNATIVES
France

CORDIS fornisce collegamenti ai risultati finali pubblici e alle pubblicazioni dei progetti ORIZZONTE.

I link ai risultati e alle pubblicazioni dei progetti del 7° PQ, così come i link ad alcuni tipi di risultati specifici come dataset e software, sono recuperati dinamicamente da .OpenAIRE .

Risultati finali

Websites, patent fillings, videos etc. (1)

Project website

Project website.

Other (1)

Data Integration Pipeline Tools

Data Integration Pipeline Tools.

Documents, reports (1)

Literature review and classification of digital health applications

Literature review and classification of digital health applications.

Pubblicazioni

Conference proceedings (5)

INSAFEDARE Project: Innovative Applications of Assessment and Assurance of Data and Synthetic Data for Regulatory Decision Support

Autori: Parisis Gallos, Nicholas Matragkas, Saif ul Islam, Gregory Epiphaniou, Scott Hansen, Stuart Harrison, Bram van Dijk, Marcel Haas, Giorgos Pappous, Simon Brouwer, Francesco Torlontano, Saadullah Farooq Abbasi, Omid Pournik, James Churm, John Mantas, Carlos Luis Parra-Calderón, Dimitrios Petkousis, Patrick Weber, Benjamin Dzingina, Chokri Mraidha, Carsten Maple, Jim Achterberg, Marco Spruit, Evi Saratsioti, Younes Moustaghfir, Theodoros N. Arvanitis
Pubblicato in: Studies in Health Technology and Informatics, Digital Health and Informatics Innovations for Sustainable Health Care Systems, 2024
Editore: IOS Press
DOI: 10.3233/SHTI240624

A Novel Taxonomy for Navigating and Classifying Synthetic Data in Healthcare Applications

Autori: Bram van Dijk; Saif ul Islam; Jim Achterberg; Hafiz Muhammad Waseem; Parisis Gallos; Gregory Epiphaniou; Carsten Maple; Marcel Haas; Marco Spruit
Pubblicato in: Studies in Health Technology and Informatics ISBN: 9781643685540, 2024
Editore: IOS Press
DOI: 10.48550/ARXIV.2409.00701

Preliminary Results on Improved Synthetic Image Generation for Melanoma Skin Cancer

Autori: Saadullah Farooq Abbasi, Muhammad Bilal, Teesta Mukherjee, Saif ul Islam, Omid Pournik, Theodoros N. Arvanitis
Pubblicato in: Studies in Health Technology and Informatics, Envisioning the Future of Health Informatics and Digital Health, 2025
Editore: IOS Press
DOI: 10.3233/SHTI250081

Characterizing a Framework for Assessment of the Use of FAIR Principles for Health Data in Digital Health Devices Regulation

Autori: Carlos Luis Parra-Calderón, Giulia Finocchiaro, Saif ul Islam, Stuart Harrison, Gregory Epiphaniou, Carsten Maple, Parisis Gallos
Pubblicato in: Studies in Health Technology and Informatics, Envisioning the Future of Health Informatics and Digital Health, 2025
Editore: IOS Press
DOI: 10.3233/SHTI250093

Deep Learning-Based Synthetic Skin Lesion Image Classification

Autori: Saadullah Farooq Abbasi, Muhammad Bilal, Teesta Mukherjee, James Churm, Omid Pournik, Gregory Epiphaniou, Theodoros N. Arvanitis
Pubblicato in: Studies in Health Technology and Informatics, Digital Health and Informatics Innovations for Sustainable Health Care Systems, 2024
Editore: IOS Press
DOI: 10.3233/SHTI240612

Peer reviewed articles (1)

Fidelity-agnostic synthetic data generation improves utility while retaining privacy

Autori: Jim Achterberg, Marcel Haas, Bram van Dijk, Marco Spruit
Pubblicato in: Patterns, 2025, ISSN 2666-3899
Editore: Elsevier BV
DOI: 10.1016/J.PATTER.2025.101287