EU Innovation Challenge

REGEN

REsilient GENerative Multimodal AI for Clinical Data

Multimodal generative AI that combines CT imaging and clinical records to complete, harmonise and synthesise clinical datasets, developed within the AI-BOOST AI Challenge Competition.

Generative AI Multimodal Clinical Data Synthetic Data Fairness

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Overview

REGEN is a Spark Phase winner of the AI-BOOST AI Challenge Competition, a European initiative funded under Horizon Europe to advance trustworthy Generative AI. The project addresses Challenge 3: Generative AI for Enhancement of Clinical Datasets, proposed by the European Federation for Cancer Images (EUCAIM), and is developed by UCBM-ArCo together with Eskişehir Osmangazi University and UniCamillus International Medical University.

Clinical datasets are often incomplete and heterogeneous: variables are missing, CT scans are unavailable for some patients, and acquisition settings differ across centres. REGEN combines CT imaging and clinical records in a multimodal generative framework that fills in missing clinical variables, reconstructs missing CT scans, harmonises scans across acquisition settings and generates high-fidelity synthetic patients, with fairness built in from the start.

Over the coming months REGEN will move from concept to a validated prototype on European supercomputing infrastructure, working towards the final demo of the AI-BOOST Advance Phase in Brussels in February 2027.

Project Objectives

Fill in missing clinical variables using CT imaging and clinical records.
Reconstruct missing CT scans from the available multimodal information.
Harmonise CT scans acquired with different settings.
Generate high-fidelity synthetic patients with fairness built in from the start.
Validate a prototype on European supercomputing infrastructure for the Brussels demo in February 2027.

Implementation Phases

Spark Phase: initial prototype with AI-BOOST technical guidance and European supercomputing
Advance Phase: development and validation programme
Final live demonstration in Brussels (February 2027)

Research Directions

Multimodal generative modelling of CT imaging and clinical records.
Imputation of missing clinical variables and reconstruction of missing CT scans.
Harmonisation of CT scans across acquisition settings.
Generation of high-fidelity, fair synthetic patients for clinical research.

About the AI-BOOST AI Challenge Competition

REGEN has been selected as a Spark Phase winner of the AI-BOOST AI Challenge Competition, taking part in Challenge 3: Generative AI for Enhancement of Clinical Datasets, proposed by the European Federation for Cancer Images (EUCAIM).

AI-BOOST is funded under the European Union's Horizon Europe research and innovation programme through Grant Agreement No. 101135737. The initiative supports trustworthy Generative AI, helping projects progress from early research and innovation stages towards validated prototypes with a target Technology Readiness Level of 4-5.

The competition is organised in two stages. During the Spark Phase, winning teams receive technical guidance and access to European supercomputing infrastructure, including CINECA's LEONARDO supercomputer, to build an initial prototype. The Advance Phase then supports development and validation, culminating in a live demonstration in Brussels in February 2027.

REGEN combines CT imaging and clinical records to fill in missing clinical variables, reconstruct missing CT scans, harmonise scans across acquisition settings and generate high-fidelity synthetic patients, with fairness built in from the start.

Funded by the European Union under GA No. 101135737. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.

More information about the AI-BOOST AI Challenge Competition