GEMINI aims to deliver validated multi-organ and multi-scale computational models for treatment decision support and improved fundamental understanding of acute strokes, both ischaemic and haemorrhagic. We will demonstrate the added benefit of these computational models in personalised disease management. Specifically, GEMINI will deliver validated, integrated multi-scale, multi-organ Digital Twin in Healthcare (DTH) models for cerebral blood and cerebrospinal fluid flow, brain perfusion and metabolism, and blood flow and thrombosis along the heart-brain axis by integrating available and newly developed dynamic, interoperable, and modular computational models. Building on these models, GEMINI will deliver validated population-based DTHs of ischemic and haemorrhagic stroke aetiology and onset, treatment, and disease progression. Utilising these population-based DTHs, GEMINI will validate five personalised subject-specific DTHs, (1) stroke treatment, and (2) disease progression DTHs for acute ischaemic stroke and (3) aneurysm treatment, (4) subarachnoid haemorrhage progression, and (5) unruptured intracranial aneurysm risk assessment DTHs for haemorrhagic stroke to guide patient care and long-term management. We will bring proof of value of digital twins by the evaluation of the ischaemic stroke treatment selection DTH in a multi-centre clinical trial, in which treatment and patient outcomes are compared in situations with and without the availability of a DTH. GEMINI will implement a project-wide structured approach for data harmonisation, curation, model validation, verification, and model certification of the DTHs. Several outcomes of GEMINI have a high value for clinical practice, medical device industry, and in enhancing research in the fields of (bio)medical and computer sciences, warranting an extensive valorisation strategy with adequate IP protection and versatile exploitation actions to enhance a wide adaptation of the results of GEMINI.

MeDiTATe aims to develop state-of-the-art image based medical Digital Twins of cardiovascular districts for a patient specific prevention and treatment of aneurysms. The Individual Research Projects of the 14 ESRs are defined across five research tracks: (1) High fidelity CAE multi-physics simulation with RBF mesh morphing (FEM, CFD, FSI, inverse FEM) (2) Real time interaction with the digital twin by Augmented Reality, Haptic Devices and Reduced Order Models (3) HPC tools, including GPUs, and cloud-based paradigms for fast and automated CAE processing of clinical database (4) Big Data management for population of patients imaging data and high fidelity CAE twins (5) Additive Manufacturing of physical mock-up for surgical planning and training to gain a comprehensive Industry 4.0 approach in a clinical scenario (Medicine 4.0) The work of ESRs, each one hired for two 18 months periods (industry + research) and enrolled in PhD programmes, will be driven by the multi disciplinary and multi-sectoral needs of the research consortium (clinical, academic and industrial) which will offer the expertise of Participants to provide scientific support, secondments and training. Recruited researchers will become active players of a strategic sector of the European medical and simulation industry and will face the industrial and research challenges daily faced by clinical experts, engineering analysts and simulation software technology developers. During their postgraduate studies they will be trained by the whole consortium receiving a flexible and competitive skill-set designed to address a career at the cutting edge of technological innovation in healthcare. The main objective of MeDiTATe is the production of high-level scientists with a strong experience of integration across academic, industrial and clinical areas, able to apply their skills to real life scenarios and capable to introduce advanced and innovative digital twin concepts in the clinic and healthcare sectors.

PRIMAGE proposes a cloud-based platform to support decision making in the clinical management of malignant solid tumours, offering predictive tools to assist diagnosis, prognosis, therapies choice and treatment follow up, based on the use of novel imaging biomarkers, in-silico tumour growth simulation, advanced visualisation of predictions with weighted confidence scores and machine-learning based translation of this knowledge into predictors for the most relevant, disease-specific, Clinical End Points. PRIMAGE implements a hybrid cloud model, comprising the of use of open public cloud (based on EOSC services) and private clouds, enabling use by the scientific community (facilitating reuse of de-identified clinical curated data in Open Science) and also suitable for future commercial exploitation. The proposed data infrastructures, imaging biomarkers and models for in-silico medicine research will be validated in the application context of two paediatric cancers, Neuroblastoma (NB, the most frequent solid cancer of early childhood) and the Diffuse Intrinsic Pontine Glioma (DIPG, the leading cause of brain tumour-related death in children). These two paediatric cancers are relevant validation cases given their representativeness of cancer disease, and their high societal impact, as they affect the most vulnerable and loved family members. The European Society for Paediatric Oncology, two Imaging Biobanks and three of the most prominent European Paediatric oncology units are partners in this project, making retrospective clinical data (imaging, clinical, molecular and genetics) registries accessible to PRIMAGE, for training of machine learning algorithms and testing of the in-silico tools´ performance. Solutions to streamline and secure the data pseudonymisation, extraction, structuring, quality control and storage processes, will be implemented and validated also for use on prospective data, contributing European shared data infrastructures.

Ventricular arrhythmias (VAs) account for the vast majority of the 250,000 cases of sudden death recorded in Europe each year. VAs occur mainly in patients with cardiomyopathy. Intra fibrotic electrical reentrant circuits are the dominant electrophysiological mechanism. Catheter ablation is the main option for invasive treatment, involving destruction of the slow conducting channels within the scar to block the electrical circuits. Challenges related to adequate catheter placement over the target area, sufficient energy diffusion into the tissue, and lack of intramyocardial dynamic mapping result in only 30-50% of patients experiencing freedom from recurrence after ablation. The aim of the CALAMAR (ChemicAL Ablation and Mapping of ARrhythmias) project is to evaluate the use of ultrasound to 1) Map the myocardium during ablation procedure using electromechanical wave imaging (EWI); 2) Validate a disruptive strategy of "chemical" ablation combining microbubbles and ultrasound to open the blood-myocardium barrier, and lipid nanoparticles loaded with cardiotoxic agents to induce a chemical dechannelization.

Air transportation is expected to grow persistently over the next decades. Clean combustion technology for aircraft engines is a key enabler to reduce the impact of this growth on ecosystems and humans’ health. The vision for European aviation is shaped by the Advisory Council for Aviation Research and Innovation in Europe in the Flight Path 2050 goals, which define stringent regulations on pollutant emissions. To meet these goals, the major engine manufacturers develop lean premixed combustors operated at very high pressure. This development introduces a large risk for reduced reliability and lifetime of engines: pressure oscillations in the combustor called thermoacoustics. Much research has been dedicated to study this phenomenon over the last decades with mixed success. Industrial experience shows that the pressure oscillations often surface as late as the full engine has been built and tested. Traditional engineering methods fall short of predictability during the design of the engines due to a high sensitivity of thermoacoustics with respect to barely known input parameters. Aviation industry encounters currently the fourth industrial revolution: cyber-physical systems analyze and monitor technical systems and take automated decisions. This industrial revolution is known as “Industry 4.0” in Germany and “Industrial Internet” in the USA. An essential enabler of the fourth industrial revolution is Machine Learning. The ITN MAGISTER will utilize Machine Learning to predict and understand thermoacoustics in aircraft engine combustors, and lead combustion research a revolutionary new approach in this area. The participation of the major aircraft engine OEMs GE, Rolls Royce, Safran ensures industrial relevance and outreach of the results. The project will shape early career talents in a network of world leading scientists and industrial partners to work on one of the most severe design issues in aviation technology in the spirit of the fourth industrial revolution.