Synthetic Generation of Hematological Data Over Federated Computing Frameworks: SCD Use Case

N/AActive Not RecruitingNCT06794281

Hospital Universitari Vall d'Hebron Research Institute1,500 enrolled

Overview

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).

SYNTHEMA will develop a federated learning (FL) infrastructure, equipped with secure multiparty computation (SMPC) and differential privacy (DF) protocols, connecting clinical centres bringing standardised, interoperable multimodal datasets and computing centres from academia and SME. This framework will be utilised to train the developed algorithms and perform SMPC-based global model aggregation in a privacy-preserving fashion. The resulting data will be validated for their clinical value, statistical utility and residual privacy risks. The project will develop legal and ethical frameworks to guarantee privacy by-design in the collection and processing of health-related personal data and attain an ethics-wise algorithm co-creation. Project outcomes, including pipelines, standards and data, will be made openly available to stakeholders in the healthcare, academia and industry field, and contribute to existing rare disease registries

Study Type

OBSERVATIONAL

Enrollment

1,500

Conditions

Sickle Cell Disease

Interventions

Generate synthetic multimodal (clinical, omics and imaging) data for rare haematological diseases with a validated clinical resultOTHER

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.

Outcomes

Primary Outcomes

Generate synthetic multimodal (clinical, omics and imaging) data for rare haematological diseases with a validated clinical result

For Sickle Cell Disease, validation scenarios will test the reliability of synthetic data in regards to genomic variants/disease phenotypes association and MRI feature-based prediction of brain vascular events (SCD).

Time frame: November 2026