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Synthetic Generation of Hematological Data Over Federated Computing Frameworks: SCD Use Case
Sponsor: Hospital Universitari Vall d'Hebron Research Institute
Summary
Haematological diseases (HDs) are a large group of disorders resulting from quantitative or qualitative abnormalities of blood cells, lymphoid organs and coagulation factors. Despite most of them (\~74%) are rare, the overall number of HD affected patients worldwide is important, placing a considerable economic burden on healthcare systems and societies. Despite the existence of several collaborative research groups at national and EU level, current clinical approaches are often ineffective, particularly for rarest conditions, due to the relatively low number of patients per disease and the high number of unconnected clinical entities. SYNTHEMA aims to establish a cross-border data hub where to develop and validate innovative AI-based techniques for clinical data anonymisation and synthetic data generation (SDG), to tackle the scarcity and fragmentation of data and widen the basis for GDPR-compliant research in rare hematological disorders (RHD). The project will focus on one representative RHD use case: sickle-cell disease (SCD).
Official title: Synthetic Generation of Hematological Data Over Federated Computing Frameworks (SYNTHEMA): SCD Use Case
Key Details
Gender
All
Age Range
1 Year - Any
Study Type
OBSERVATIONAL
Enrollment
1500
Start Date
2022-11-01
Completion Date
2026-11-30
Last Updated
2025-03-12
Healthy Volunteers
No
Conditions
Interventions
Generate synthetic multimodal (clinical, omics and imaging) data for rare haematological diseases with a validated clinical result
O1. Provide novel methods and capabilities to generate synthetic multimodal clinical, omics and imaging data for SCD with a validated clinical result. O2. Develop de-identification, minimisation and anonymisation pipelines, including automatic assessment of privacy levels, at the service of clinical research and care. O3. Consolidate and scale-up the use of FL applications, SMPC and DP solutions for privacy-preserving local algorithm training and global model aggregation. O4. Ensure ethical and GDPR compliance in anonymised and synthetic data-driven research in RHDs. O5. Ensure wide uptake and scalability of the developed methodologies and tools through effective stakeholder engagement, dissemination and open science practices.
Locations (3)
Azienda Ospedale Università Padova
Padova, Italy
UMC Utrecht
Utrecht, Netherlands
Vall Hebron Institut de Recerca
Barcelona, Barcelona, Spain