Frontotemporal dementia (FTD) has a debilitating effect on patients and their caregivers and leads to substantial economic costs. 15-30% of patients have familial FTD caused by known pathogenetic mutations. For the other 70-85% of patients, termed sporadic FTD, diagnosis is slow (~3.6 years) with frequent misdiagnosis due to clinical, genetic and molecular heterogeneity. Thus, there is great need for biomarkers for early diagnosis of sporadic FTD and its pathological subtypes. In PREDICTFTD, we will validate a set of biomarkers and create a diagnostic tool for early diagnosis of familial and sporadic FTD, which will facilitate tailored support and symptomatic treatments and care. We will apply several new approaches to achieve this: 1) we combine 11 geographically diverse cohorts of sporadic and familial FTD with retrospective and prospective longitudinal liquid biopsy samples and extensive clinical and behavioural data; 2) we are the first to use multimodal clinical and liquid biomarker data to train an AI-algorithm as a diagnostic tool for quick and early clinical FTD diagnosis; and 3) we implement a novel robust two-stage strategy for biomarker and AI algorithm validation, where phase I validates biomarkers and algorithms on a cohort of genetic and autopsied cases and phase II assesses biomarker value for diagnosis of sporadic FTD and at-risk pre-symptomatic mutation carriers. We will apply this two-stage validation strategy to address three critical clinical challenges: i) To distinguish sporadic FTD from (non-) neurodegenerative disorders that show significant clinical/symptomatic overlap, ii) To robustly detect FTD pathological subtypes in sporadic FTD and iii) pre-symptomatic identification of FTD onset. Thus, PREDICTFTD will transform FTD diagnosis, offering potential for early disease confirmation, guiding treatment decisions, facilitating patient recruitment for clinical trials, guidance of patients and caregivers, and enabling preventive measures.

Dementia is caused by a range of illnesses and disorders that damage the brain either directly or indirectly. With the rise of the ageing population in the EU, dementia is becoming a serious problem. Digital health interventions have the potential to improve the accessibility and effectiveness of palliative care. Palliative care is an area where these technologies are increasingly being evaluated for education (e.g. online learning, mobile applications or Virtual Reality tools), symptom management, care planning, decision-making, and interaction (e.g. professionals and caregivers using phones, internet and computer systems). However, most studies focus on a specific intervention with heterogeneous outcomes and are exposed to professional gatekeeping and biased samples consisting of patients who are mostly well and without considering cultural impacts. Due to improved understanding and treatment, more effective and innovative health technologies, improved patient safety and better ability and preparedness to manage epidemic outbreaks, along with priorities related to quality of life of dementia patients and survivors, treatment and dementia data monitoring should be crucial. This project will focus on: i) better understanding of dementia, focusing on their consequences, including pain, distress and causative links between health determinants, disease and interventions in order to provide evidence-base for policy-making, ii) identification of holistic intervention (treatment and care) and assessment of health outcomes, iii) innovative digital tools to optimize clinical workflows and iv) scientific evidence for improved/tailored policies and legal frameworks and to inform major policy initiatives at EU and global level. We target exactly those aspects of value by integrating digital interventions as palliative care of patients with poor prognosis of dementia and evaluating the impact of digital health interventions using Artificial Intelligence.

REBECCA, a heavily SME-driven project, will democratize the development of novel edge AI systems. Towards this aim, REBECCA will develop a purely European complete Hardware(HW) and Software(SW) stack around a RISC-V CPU, which will provide significantly higher levels of a) performance (e.g., inferences per second), b) energy/power efficiency (e.g., inferences per joule/watt), c) safety and d) security than the existing ones. This will be achieved by utilizing state-of-the-art technologies and by making significant scientific and technological advances in several key relevant domains, including a) processing units, b) hardware accelerators, c) reconfigurable hardware, d) tightly coupled interconnected chiplets e) HW/SW co-design and co-development tools, f) system software, g) middleware, and h) AI libraries and frameworks. REBECCA will significantly contribute to realizing business and societal opportunities by validating and demonstrating its approach on 4 real-world use cases and 2 benchmarks based on real-world applications from the Smart appliances, Energy Generation, Infrastructure Inspection, Avionics Automotive and Health domains. In terms of HW, REBECCA will develop a novel chip consisting of two tightly coupled chiplets which will incorporate: a) RISC-V multicore, b) Neuromorphic AI Accelerator, c) Programmable array AI Accelerator, d) AI Accelerator utilizing a hierarchical processing architecture, e) DNN Accelerator, f) Reconfigurable hardware, g) Near-Memory-Processing, h) Memory Encryption. In terms of SW, REBECCA will implement optimized system SW, middleware, and AI libraries that will take full advantage of the underlying novel HW. The REBECCA platform will be complemented by a novel HW/SW Design Space Exploration tool which will allow the development of highly efficient REBECCA-based systems. REBECCA will additionally provide the means for safety and security modeling and verification for the developed HW and SW from the very early design stages.

Independent validation is fundamental to emphasise the capability and safety of any solution in the electric, connected and automated (ECA) vehicles space. It is vital that appropriate and audited testing takes place in a controlled environment before any deployment takes place. As the software and hardware components come from multiple vendors and integrate in numerous ways, the various levels of validation required must be fully understood and integration with primary and secondary parts must be considered. The key targets of ArchitectECA2030 are the robust mission-validated traceable design of electronic components and systems (ECS), the quantification of an accepted residual risk of ECS for ECA vehicles to enable type approval, and an increased end-user acceptance due to more reliable and robust ECS. The proposed methods include automatic built-in safety measures in the electronic circuit design, accelerated testing, residual risk quantification, virtual validation, and multi-physical and stochastic simulations. The project will implement a unique in-vehicle monitoring device able to measure the health status and degradation of the functional electronics empowering model-based safety prediction, fault diagnosis, and anomaly detection. A validation framework comprised of harmonized methods and tools able to handle quantification of residual risks using data different sources (e.g. monitoring devices, sensor/actuators, fleet observations) is provided to ultimately design safe, secure, and reliable ECA vehicles with a well-defined, quantified, and acceptable residual risk across all ECS levels. The project brings together stakeholders from ECS industry, standardization and certification bodies (e.g. ISO, NIST, TUEV), test field operators, insurance companies, and academia closely interacting with ECSEL lighthouse initiative Mobility.E to align and influence emerging standards and validation procedures for ECA vehicles.

In the face of a rapidly advancing digital healthcare terrain, the DTRIP4H project emerges as a momentous effort to revolutionize predictive, preventive, personalized, and participatory health paradigms within the EU. Amid significant incidence of chronic conditions and cancer, there is a pressing need for a proactive shift in health strategies. Yet, the full potential of European research infrastructures (RIs) is curtailed by investment deficits, fragmentation, and the intricacies of data management. Digital Twin (DT) technology introduces a new age of precision by enabling sophisticated simulations and analyses of intricate biological processes. In DTRIP4H, we start a new initiative in Europe “decentralized health digital twin ecosystem consisting of RIs”. Using DTs, we aim to resolve critical challenges around data harmonization, equitable access, and stringent privacy safeguards. Incorporating technologies such as federated learning, Generative AI, and Virtual Reality (VR), the project aspires to create a decentralized digital twin environment (DDTE). This will empower both internal and external RI users, such as researchers, innovators, and SMEs, to craft DT applications that address specific scientific challenges, utilizing a blend of real-world and synthetic data in compliance with regulatory frameworks, i.e. GDPR. We will develop 7 innovative proof of concept thematic health-related Use cases fulfilling the needs of scientists, SMEs, and industrial end users, particularly in health topics related to cancer treatment, drug development, human environmental exposome, precision treatment for schizophrenia and personalized medicine through Artificial Intelligence (AI), AR/VR empowered DTs utilizing DDTE, while adhering to FAIR data principles. DTRIP4H adopts a human-centric methodology to elevate research efficacy, narrow the skills gap, and align with the objectives of the European Research Area (ERA) and the Sustainable Development Goals (SDGs) by 2030.