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.
Role:We will run a prediction use case with pre-existing data from AUTH. In this use case we will try to identify how the IPRs can be shared for algorithms development by a company with datasets that belong to another organization as well as the ethical issues that might arise from the secondary use of data. The goal of the use case will be the prediction of progression of Multiple Sclerosis.
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.
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 heterogenous (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.
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.