Project Vision: EOSC Web of FAIR Data and Services for Science is an open, fair and reliable Research Community where every researcher will be accredited for their work and all research data will be equally accessible for processing without violating data protection regulations. In line with this vision, the mission of RAISE is to provide the infrastructure for a distributed crowdsourced data processing system, moving from open data to open access data for processing. RAISE will provide the mechanism for sending the algorithm to the dataset instead of sending the data to the algorithm.
Project Vision: RELEVIUM is an EU-funded project under the HORIZON-EUROPE framework. RELEVIUM’s main objective is the development of multi-modal interventions for the improved palliative care of cancer patients. RELEVIUM focuses on Pancreatic Ductal Adenocarcinoma (PDAC) patients for which different barriers to effective supportive and palliative care reduce their Quality of Life and overall survival. RELEVIUM aims to address these issues by proposing a multimodal supportive intervention based on continuous remote monitoring technologies for (i) pain estimation, (ii) estimation of muscle mass/sarcopenia, (iii) monitoring of nutrition, as well as (iiii) physical activity of advanced PDAC patients. The project will first conduct a feasibility and data collection study, and then a randomized clinical trial involving 5 clinical cancer centres in Germany, France, Belgium, Estonia and Israel. These studies will assess the effectiveness of the proposed interventions in improving the quality of life of advanced PDAC patients. Furthermore, several important parameters will be explored, such as (i) the cost-effectiveness of the proposed solution, (ii) its potential in increasing health equity across the populations of the participating countries, in terms of access to supportive and palliative care for advanced PDAC patients and (iii) the stress burden of the disease on the families and family caregivers of the patients. With these strategies and the proposed AI-based solutions, RELEVIUM aims to empower PDAC patients to self-manage their disease, reflect and optimize their individual QoL, and receive improved clinician guidance, independently of their home and country. AINIGMA has an active role in the project, through the research and development of a sophisticated algorithm for sarcopenia estimation and prediction.
Project Vision: iPROLEPSIS aspires to shed light upon the health-to-Psoriatic Arthritis (PsA) transition with a comprehensive multiscale/multifactorial PsA model employing novel trustworthy AI-based analysis of multisource and heterogeneous (i.a., in-depth health, environmental, genetic, behavioural) data, digital phenotyping of inflammatory symptoms with emphasis on tracking of motor manifestations using smart devices and wearables, novel optoacoustic imaging-based markers of PsA in the skin and joints, and investigation of the role of mast cells in the PsA transition, to identify key drivers of the disease and support personalized models for PsA risk/progression prediction and monitoring as well as associated inflammation detection and severity assessment. AINIGMA will create dynamic, multiscale/multifactorial, and personalised xAI-driven models of the health-to-PsA inflammation transition that will enable prediction of risk and early diagnosis of PsA in people at risk, with emphasis on patients with psoriasis (PSO), as well as prognosis of disease progression targeting the prevention of PsA inflammation exacerbation.
Project Vision: ONCOSCREEN will develop a risk-based, population-level stratification methodology for Colorectal Cancer (CRC), to account for genetic prevalence, socio-economic status, and other factors. It complements this methodology by a) developing a set of novel, practical, and low-cost screening technologies with high sensitivity and specificity, b) leveraging AI to improve existing methodologies for CRC screening, allowing for the early detection of polyps and provision of a personalized risk status stratification, and c) providing a mobile app for self-monitoring and CRC awareness raising. Furthermore, ONCOSCREEN develops an Intelligent Analytics dashboard for policy makers facilitating effective policy making at regional and national levels. Through a multi-level campaign, the above-mentioned solutions are tested and validated. For the clinical solutions specifically, a clinical validation study has been planned with the participation of 4100 enrolled patients/citizens. AINIGMA will participate in the development of AI Algorithms for Polyp Detection & Classification from Real-time Colonoscopy images. Most cases of CRC start as a growth on the colorectal epithelium, called polyp. CRC can be prevented with the early detection and recognition of the type of polyps, and colonoscopy is the main diagnostic procedure. However, the visual detection and classification of polyps can be challenging due to several factors, including the illumination conditions of colonoscopy, the variability of texture and appearance, and the overlapping morphology between polyps. Moreover, the evaluation of polyp patterns by gastroenterologists is subjective leading to a relatively high interobserver variability. Even for experienced endoscopists, working on conventional colonoscopy for long hours leads to mental and physical fatigue and degraded analysis and diagnosis. Within the ONCOSCREEN project, AINIGMA will participate in the development of Deep Learning algorithms for the accurate detection and classification of colorectal polyps, with the aim to develop a computer-aided diagnostic tool for the improved screening of CRC.
Project Vision: BioStreams aims on supporting the optimal use (and re-use) of health data (e.g. biological, demographic, epigenetic, etc.) to generate metadata/knowledge and provide new evidences, methodologies & tools for: a) creating and deploying the Bio-Streams Biobank that will act as a scientific platform for research in obesity and better diagnostic and therapeutic approaches; b) understanding the transition from metabolically healthy to unhealthy, preventing under age obesity; c) designing better strategies to educate & empower young citizens for weight self-management; d) coordinating authorities & policy makers to develop cross-sectoral solutions for health promotion & under age obesity prevention. Specifically, Bio-Streams delivers a dedicated underage obesity biobank providing real-world health data (including biospecimens, anthropometrics, behavioural and cost data) from retrospective and prospective sources, while taking into account efficient data harmonisation and standardisation principles, transforming data valorisation towards underage obesity prevention and future research. Moreover, the Bio-Streams framework for data handling, provides robust and transparent methodologies for operational procedures (data infrastructures), analysis and reporting (via meta-reviews and AI-based Apps/components, evaluated via 12 multi-site pilots (7 clinical and 5 schools), citizen awareness and lifestyle alteration, delivering obesity prevention guidelines, knowledge generation while fostering considerable opportunities for regional and national health authorities/policymakers. Within BIO-STREAMS, AINIGMA is responsible for the development of an AI-powered Risk Stratification Tool and Recommendation Engine, aiming to improve management of obesity in childhood and adolescence.
Project Vision: Following a trustworthy and inclusive approach to AI development, multidisciplinary expertise and broad stakeholder engagement, AI-PROGNOSIS aims to advance PD diagnosis and care by: i) developing novel, predictive AI models for personalized PD risk assessment and prognosis (regarding time to higher disability transition and response to medication) based on multi-source patient records and databases, including in-depth health, phenotypic and genetic data ii) implementing a system of digital biomarkers informing the AI models by tracking key risk/progression markers in daily living, and ultimately and iii) translating the models and digital biomarkers into a validated, privacy-aware AI-driven toolkit, supporting healthcare professionals (HCPs) in disease screening, monitoring and treatment optimization, via quantitative, explainable evidence, and empowering individuals with/without PD with tailored insights for informed health management
Project Vision: The objective of DIOPTRA is to tackle certain challenges in Colorectal Cancer (CRC) detection, screening, and progression by introducing advanced technologies. DIOPTRA aims to develop a primary screening tool for identifying individuals at high risk for colorectal cancer (CRC) based on risk factors and protein biomarkers. Blood and tissue samples will be analyzed to identify a specific set of prognostic proteins that can be detected through standard blood tests, indicating the need for further evaluation, such as colonoscopy. In addition to medical data, behavioral data will also be considered as a potential risk factor. Artificial intelligence (AI) will be utilized to evaluate the prognostic power of the biomarkers, while personalized behavioral change strategies will be implemented to address modifiable risk factors. The project will also explore the effectiveness of network modeling and AI-based decision support systems for early CRC detection. AINIGMA has an active role in the part of AI utilization in the project, through the implementation of ML models and multi-modal data fusion for the development of a robust and explainable AI-powered Decision Support System for CRC screening.
Project Vision: PREVENT aims to enhance the implementation of primary interventions for managing weight control during childhood and adolescence, with the objective of reducing cancer risks in adulthood. This approach is based on current evidence linking excess body weight with an increased risk of cancer. To achieve this goal, PREVENT adopts several implementation research strategies. First, it identifies the obstacles that prevent current interventions and policies from being scaled up to various geographical, socio-economic, and cultural settings. Then, it introduces new interventions that are context-sensitive and multi-stakeholder in nature, along with innovative user engagement tactics to overcome existing scaling-up difficulties. Within PREVENT, AINIGMA is responsible for the development of trustworthy AI-powered toolkits for the improved data processing and analysis.
Project Vision: NERO is an advanced Cybersecurity Ecosystem consisting of five interrelated frameworks provisioned to offer a Cybersecurity Awareness program, as recommended by ENISA to be the best way to educate and develop a security-first culture amongst employees on how to mitigate the impact of cyber threats and incorporate activities, resources, and training to foster a cyber security culture. NERO primarily provides SMEs with a Cyber Immunity Toolkit Repository, Cyber Resilience Program, and Cyber Awareness Training via Gamification and provides its modules through a user-friendly Marketplace. The effectiveness and the performance of the concept will be validated in three use case demonstrations on different domains: Enhancing Patient Data Security in Healthcare through Cybersecurity Tools; Strengthening Supply Chain Resilience through Cybersecurity Awareness in the Transportation and Logistics Industry; and Boosting Financial Security through Enhanced Cybersecurity Awareness and Tools.
Project Vision: PHASE IV will advance the current state-of-the-art data synthesis methods towards a more generalized approach of synthetic data generation. We will also develop metrics for testing and validation, as well as protocols that enable synthetic data generation without access to real-world data (through multi-party computation). We aim to provide: 1) Improved methods and technical pipelines for privacy-preserving data synthesis including different data formats such as EHRs and medical images, 2) Easy to use and configurable data services to enable AI developers’ access to larger pools of decentralized de-identified data through multi-party computing, 3) Provide anonymous data on demand or from a (temporary) repository, 4) Establish a Data Market – facilitating data sharing and monetization incl. incentives-based provision of data to the services, 5) Integrate the data market and the data service ecosystem as a X-European health data hub in the European Health Data Space, and 6) Validate the results with real-world use-cases focusing on high impact diseases, cancer types in particular.
Project Vision: The impact of climate change has raised serious concerns about imminent health risks, with research highlighting the potential extent of these dangers. However, there has been little effort to introduce and implement policies and technologies to alleviate these health risks. The EU-funded ISMED-CLIM project aims address this gap by developing novel solutions for mitigating health risks associated with climate change, with ffocus on the Mediterranean region. The project will investigate the impact of climate change on non-communicable diseases and vector-borne infections, examine current public health efforts and measures, and collaborate with experts to develop prediction tools, treatment strategies, and methodologies. These solutions will undergo testing in Cyprus, Greece, Italy, Portugal, and Spain. AINIGMA is responsible for the data harmonization of the health and climate-related data and plays a key role in the developing the ISMED-CLIM Early Warning System for notifications and recommendations.
Project Vision: VOLABIOS harnesses cutting-edge technologies to advance early schizophrenia (SZ) diagnosis and unravel its molecular mechanisms. By integrating multi-omics approaches—genomics, proteomics, metabolomics, and transcriptomics—it provides a comprehensive molecular profile of the disease. Mobile spectrometry (volatolomics) enables non-invasive detection of volatile organic compounds emitted by the body, while AI-driven data analysis identify key biomarkers, enhancing diagnostic accuracy and efficiency. This innovative framework enables precise and rapid identification of chemical and biochemical signals serving as biomarkers for schizophrenia accelerating early detection, deepens disease understanding, and improves patient outcomes. AINIGMA leads the retrospective data collection and analysis, translating AI-analyzed SZ dataset findings into actionable insights and managing the project’s data strategy.
Project Vision: Extreme natural events, such as the 2021 boreal wildfires, underscore the importance of systemic action and reveal the vulnerability of already marginalised populations. Climate change affects human health and well-being, impacting Europe as a whole. The increasing frequency and severity of extreme events have far-reaching consequences for local communities. The EU-funded AURORA project aims to enhance resilience against health risks stemming from climate change. The project will achieve this by developing a suite of tools capable of monitoring climate stressors, creating climate and epidemiological models, generating forecasts via simulated scenarios, and identifying climate change risks and vulnerabilities in EU boreal regions. The project integrates AI-driven technology to recommend adaptive measures and nature-based solutions to contrast the effects of climate change. AINIGMA is responsible for the data harmonization of the health and climate-related data and has an active role in the implementation of the AI-based climate-related risk assessment tool and the Early Warning System.
Project Vision: Transnational reports indicate that Social Economy employees, on average, receive lower compensation, experience more precariousjobs, and face informal working conditions. Therefore, there is an urgent need to broaden the scope of social impact assessment beyond service beneficiaries, incorporating the impact on SE employees. Through ASSETS, technological advancements hold the potential to support SE progression, with platform cooperatives emerging asinfluential contributors committed to citizen involvement and improving members‘ working conditions through collaborative governance. ASSETS employs a four-step strategy to explore SE’s impact on social inclusion, quality job provision, and sustainability: conducting research and case studies spanning regions both within and outside the European Union, improving evaluation tools through local integration and technological advancements, and creating a digital collaborative platform to engage stakeholders and enhance working conditions within SE.
Project Vision: The HERA (Helping Empower Women through Reskilling and Advancement) project is designed to actively increase the participation of women over 45 years old in adult education, by developing and promoting the culture of lifelong learning as a catalyst for equality, inclusion, social cohesion, economic growth, and prosperity in a Europe characterized by dynamic adaptation to the twin digital and green transitions. The project ecosystems will combine the approaches of mutual aid groups, living labs, and innovation hubs innovatively, creating resilient communities by highlighting the concept of mutual support. The results of this community approach will be systematically measured and transferred to an online environment, where a virtual Community Portal will be developed using AI technology to scale up the project’s impact, to other intervention zones and productive sectors. The evidence-based results of the project will formulate specific Policy Recommendations, involving key stakeholders and policy makers in each country, for the adaptation of the HERA approach at the national and European levels.
Project Vision:RAISE Suite aims at removing barriers to management and sharing of research data. The project will develop the solutions required to automate the process from data collection to dataset generation, guided by a FAIR-by-design principle, which will speed-up and ease research data sharing. Simultaneously, EOSC-RAISE will be integrated into RAISE Suite, for a platform which supports simple dataset sharing and exploitation, mitigating the sense of lack of recognition and data protection among researchers. Furthermore, RAISE Suite will implement a DMP-guided data collection and management policy. The project will adopt a Machine Actionable Data Management Plan and extend it, e.g., by turning the persistent identifier DMP-ID into the main reference point for the whole data lifecycle.
Project Vision: Ovarian cancer (OC) is the most lethal of female cancers, often termed a “silent killer”. DISARM’s overall approach to tackle the significant gaps in hereditary OC management lies in tackling both key elements of risk assessment and early detection. The project will investigate multifactorial risk assessment versus standard practices in 4 EU Member States (MS) (Lithuania, Portugal, Czech Republic, and Greece), and will upscale and validate a set of easy-to-use, highly accurate and affordable technologies in five countries (UK, Lithuania, Portugal, Czech Republic, and Greece). Several intelligent digital assets will optimally support and enhance our clinical studies, while a range of multifaceted activities will ensure the future uptake and adoption of DISARM solutions. The project aligns with the Innovation Action character of this topic by focusing on both mature technologies that can be upscaled in routine healthcare and on emerging technologies that have already shown a potential to justify larger scale validation activities. Our ultimate ambition is to holistically investigate the preconditions and set the stage for rolling out proven solutions in routine OC risk assessment, and in parallel to create further evidence for the introduction of novel promising elements in early detection programmes. DISARM gathers 26 partners from 12 countries (10 EU MS, the UK and Canada), thereby exhibiting a significant geographic coverage, strengthening European and international collaboration and ensuring widespread diffusion of the project results. This action is part of the Cancer Mission cluster of projects on ‘Prevention and Early Detection (early detection heritable cancers).
Partners and Collaborators:
Neureka 2020 Epilepsy Challenge
AINIGMA’s team was part of the BIOMED IRREGULARS’ team from KU LEUVEN who was awarded the 1st prize in NEUREKA 2020 for epilepsy seizure detection using EEG Seizure dataset
