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

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

ACTIVE_NOT_RECRUITING

Total Enrollment

1500 participants

Study Classification

OBSERVATIONAL

Study Start Date

2022-11-01

Study Completion Date

2026-11-30

Brief Summary

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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).

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Detailed Description

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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

Conditions

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Sickle Cell Disease

Study Design

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Observational Model Type

COHORT

Study Time Perspective

CROSS_SECTIONAL

Interventions

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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.

Intervention Type OTHER