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

Vall d'Hebron University Hospital (HUVH)

Status

Active, not recruiting

Conditions

Sickle Cell Disease

Treatments

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

Study type

Observational

Funder types

Other

Identifiers

NCT06794281

PR(AMI)205/2023

Details and patient eligibility

About

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).

Full description

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

Enrollment

1,500 estimated patients

Sex

All

Ages

1+ year old

Volunteers

No Healthy Volunteers

Inclusion criteria