Common mechanisms and pathways in Stroke and Alzheimer's disease. It has long been recognized that stroke and (Alzheimer’s Disease) AD often co-occur and have an overlapping pathogenesis. As such, these two diseases are not considered fellow travelers, but rather partners in crime. This multidisciplinary consortium includes epidemiologists, geneticists, radiologists, neurologists with a longstanding track-record on the etiology of stroke and AD. This project aims to improve our understanding of the co-occurrence of stroke and AD. An essential concept of our proposal is that stroke and AD are sequential diseases that have overlapping pathyphysiological mechanisms in addition to shared risk factors. We will particularly focus on these common mechanisms and disentangle when and how these mechanisms diverge into causing either stroke, or AD, or both. Another important concept is that mechanisms under study will not only include the known pathways of ischemic vasculopathy and CAA, but we will explore and unravel novel mechanisms linking stroke and AD. We will do so by exploiting our vast international network in order to link various big datasets and by incorporating novel analytical strategies with emerging technologies in the field of genomics, metabolomics, and brain MR-imaging.

ArtifArtificial Intelligence (AI) will revolutionize healthcare as its diagnostic performance approaches that of clinical experts. In particular, in cancer screening, AI helps patients to make better-informed decisions and reduce medical error. However, this requires large datasets whose collection faces severe practical, ethical and legal obstacles. These obstacles can be overcome with swarm learning (SL) where partners jointly train AI models without sharing any data. Yet, access to SL technology is seriously limited because no studies have implemented SL in a true multinational setup, no practically usable implementation of SL is available, researchers & healthcare providers have no experience with setting up SL networks and policymakers are currently unaware of the broader implications of SL. ODELIA will address & solve these issues: ODELIA will build the first open-source software framework for SL, providing an assembly line for the streamlined development of AI solutions. To serve as a blueprint for future SL-based AI systems, ODELIA partners collaborate as a swarm to develop the first clinically useful AI algorithm for the detection of breast cancer in magnetic resonance imaging (MRI). The size of ODELIA's distributed database will exceed all previous studies and ODELIA's AI models will reach expert-level performance for breast cancer screening. Thereby, ODELIA will not only deliver a useful medical application, but prove the clinical benefit of SL in terms of accelerated development, increased performance and robust generalizability to ultimately save thousands of lives of European patients. ODELIA's success will push partners to serve as nuclei for the exponential growth of the SL network and extend SL to a multitude of medical applications. Thus, patients, healthcare providers and citizens in Europe will be provided with a digital infrastructure that enables development of expert-level AI tools on big data without compromising data safety and data privacy.

Breast cancer is now the most common cancer worldwide, surpassing lung cancer in 2020 for the first time. It is responsible for almost 30% of all cancers in women and current trends show its increasing incidence. Neoadjuvant chemotherapy (NAC) has shown promise in reducing mortality for advanced cases, but the therapy is associated with a high rate of over-treatment, as well as with significant side effects for the patients. For predicting NAC respondents and improving patient selection, artificial intelligence (AI) approaches based on radiomics have shown promising preclinical evidence, but existing studies have mostly focused on evaluating model accuracy, all-too-often in homogeneous populations. RadioVal is the first multi-centre, multi-continental and multi-faceted clinical validation of radiomics-driven estimation of NAC response in breast cancer. The project builds on the repositories, tools and results of five EU-funded projects from the AI for Health Imaging (AI4HI) Network, including a large multi-centre cancer imaging dataset on NAC treatment in breast cancer. To test applicability as well as transferability, the validation with take place in eight clinical centres from three high-income EU countries (Sweden, Austria, Spain), two emerging EU countries (Poland, Croatia), and three countries from South America (Argentina), North Africa (Egypt) and Eurasia (Turkey). RadioVal will develop a comprehensive and standardised methodological framework for multi-faceted radiomics evaluation based on the FUTURE-AI Guidelines, to assess Fairness, Universality, Traceability, Usability, Robustness and Explainability. Furthermore, the project will introduce new tools to enable transparent and continuous evaluation and monitoring of the radiomics tools over time. The RadioVal study will be implemented through a multi-stakeholder approach, taking into account clinical and healthcare needs, as well as socio-ethical and regulatory requirements from day one.