Weight problems and obesity are increasing at a rapid rate already concerning more than 436mio people in European countries. Obese persons have a 50% higher risk of cardio-vascular disease (CVD) mortality and treatment costs result in a total economic burden of over 210 billion Euro per year. To date the prediction of the individual risk for major adverse CVD events in the obese patient population is a challenge. Current risk scores are not sufficiently accurate and there is no implementation of scores into user friendly solutions. The AI-POD project aims to reduce the number of CVD related deaths in Europe by developing an AI-based risk prediction score to support clinical decision making and by equipping obese persons with trustworthy AI tools. AI tools will integrate clinical, laboratory and imaging data to translate disease risk into actionable health information to guide diagnostic steps and treatment recommendations. The tools will be validated in six clinical sites on CVD and serve as the basis for a lasting interdisciplinary platform for distributed learning in other vascular territories. AI-POD will push the boundaries of clinical insight in CVD in obese persons, including its treatment and risk management. AI-POD main outcomes are (1) a novel imaging-based AI-based risk score and Clinical Decision Support System (CDSS) for the risk assessment and prediction of obesity-related CVD and associated complications as a pre-requiste for further AI-based prevention and treatment management; (2) an innovative, easy-to-use mobile app for citizens (Citizen App) that interacts with the CDSS empowering obese people to better monitor and manage their own health. Physicians will benefit from more efficient workflows and in consequence, public health budgets will be unburdened by reducing morbidity and mortality of obese indiviudals.

The central PANCAIM concept is to successfully exploit available genomic and clinical data to improve personalized medicine of pancreatic cancer. PANCAIM’s concept is unique as it integrates the whole spectrum of genomics with radiomics and pathomics, the three future pillars of personalized medicine. The integration of these three modalities is very challenging in the clinic, but also with AI. PANCAIM uses an explainable, data-efficient, two-staged AI approach. AI biomarkers transform the unimodal data domains into interpretable likelihoods of intermediate disease features. A second AI layer merges the biomarkers and responds with an integrated assessment of prognosis, prediction and monitoring of therapy response, to assist in clinical decision making. PANCAIM builds on four key concepts of AI in Healthcare: data providers, clinical expertise, AI developers, and MedTech companies to connect to data and bring AI to healthcare. Data quantity and quality is the main factor for successful AI. Partners provide eleven Pan European repositories of almost 6000 patients that are open to ongoing accrual. SME Collective Minds builds the GDPR data platform that hosts the data and provides a trustable connection to healthcare for even more and sustainable data. SME TheHyve builds tooling to connect to more genomic repositories (EOSC Health). Six Pan European academic centers provide clinical expertise across all modalities and help realize a curated, high quality annotated data set. Partners also include expert AI healthcare researchers across all clinical modalities with a proven track record. Finally, Siemens Healthineers provides their AI expertise and tooling to bring AI into healthcare for clinical validation and swift clinical integration in 3000 health care institutes.

Computerised Tomography (CT) scan is one of the most common medical imaging performed in healthcare, Each year, 300 million CT scans are performed globally. Of which, around 180M include use of radiocontrast media (RCM). Contrast Enhanced CTs (CECT) create a significant environmental impact, namely: 42,000 tonnes of single use packaging, 900 Tonnes of surgical steel (needles), 90,000 tonnes of plastic tubing and 150,000,000 kWh in energy consumption. These generate on average 9.2 kg of CO2/ scan. In addition, CECTs generates 200,000 tonnes of iodine contamination in water/yr. This is a recognised form of ‘pharmaceutical pollution’. CECTs may also harm patients: needle insertion, toxicity of iodinated RCMs to kidneys (potentially kidney failure) and allergic reactions, which in some cases can be life-threatening. Healthcare systems are responsible for the 4.4% CO2 global emissions (2 Giga tonnes/yr). Of this, ~3 Mega tonnes/yr are generated from CECTs. The EU has declared its NetZero targets of by 2050 through the European Green Deal. We showed feasibility that artificial intelligence (AI, deep learning methods) can extract high level information from non-contrast CT scans and synthesise contrast ‘digitally’. This avoids the need to administer RCM for CECTs. We seek to develop and validate 5 uses cases of CT ’Digital Contrast’ during this Horizon award. By implementing ‘Digital Contrast’ for scans globally, we aim to reduce 30% of the CO2e and iodine RCM waste generated from CECTs by 2033. NetZeroAICT has a grand vision to define a reference framework for scalable development of AI health tools for a future of sustainable health systems. This builds on our prior efforts of AICT consortium, which was established to make CT imaging safer, more efficient, more equitable and more sustainable. NetZeroAICT will accelerate the EU’s trajectory towards NetZero and advance EU’s globally recognized leadership position on Healthcare sustainability.

Patients at risk or diagnosed with arthritis are constantly assessed by innovative imaging techniques to document the onset or progression of their disease. However, despite their impressive abundance and resolution, these images lack the analysis and interpretation tools necessary to deliver unbiased and precise diagnosis, monitoring and prognosis to the patients. Additionally, some key advanced imaging methodologies such as ultrasound are hardly accessible to most of patients, urging improvements of more accessible imaging methods. The AutoPiX project is an ambitious international multi-stakeholder effort led jointly and synergistically by academic and industry partners to achieve breakthroughs in both the applicability and harnessing of imaging technologies for the benefit of patients by creating new powerful analysis and decision tools. We will first generate tools to transform unstructured images into quantitative biomarkers using artificial intelligence (AI) and machine learning (ML) models, and validate them clinically for their diagnosis, monitoring and prognosis power. This will considerably increase the utility of imaging biomarkers for arthritis and bring them to the same level as laboratory biomarkers. In parallel we will develop accessible imaging strategies such as remote monitoring and robotic-powered point-of-care ultrasound exams for patients to mitigate the often-observed shortage of qualified personnel in real world settings. To achieve this, we will improve the precision and interpretability of these methods and further validate them with clinical, molecular and histological analyses. Our consortium is built on multi-disciplinarity and the constant synergistic interaction of all the actors of arthritis care: rheumatologists, radiologists, patients, researchers, regulators, industries and small- and medium sized enterprises (SMEs). On the long term, AutoPiX will create new clinically-validated methods to achieve a/ more precise, accessible and effective diagnosis, b/ shortened and better-tailored treatment paths and c/ improved treatment response assessments and outcome prediction for patients with arthritis.

Pancreatic cancer (PDAC) is usually detected at late stages and most patients die within one year after diagnosis. In PANCAID we will therefore develop a blood test for early detection of PDAC. Despite tremendous technological advances in Liquid Biopsy Diagnostics (LBx), this goal is very ambitious because small tumors release only minute amounts of cells or cellular products (e.g., DNA, RNA, protein, metabolites) into the circulation. Thus, tests with a high sensitivity are required but increases in sensitivity are usually achieved on the expenses of reduced specificity which can lead to significant overdiagnosis leading to unnecessary stress for the individuals with a false-positive blood test and high costs for the health system. In PANCAID, we will therefore establish a blood test with high accuracy by analyzing large cohorts of patients with PDAC and its precursor lesions, individuals at risk to develop PDAC and appropriate age-matched control groups (healthy and non-cancer diseases frequent in the targeted population). Ambitious objectives of PANCAID include (1) establishment of a unique resource of blood samples of early PDAC and risk groups (WP1); (2) Establishment of a breakthrough blood test for early diagnosis of PDAC (WP2); (3) Identification of the best composite biomarker panel by integrating multimodal features in an AI-assisted computational analysis; (4) Analysis of the socio-economic impact of early PDAC diagnosis (WP4); and (5) Definition of the ethics parameters relevant to early PDAC detection (WP5). A robust multi-biomarker panel will be determined during the training period (year 1-3) and subsequently validated on bio-banked blood samples (year 4-5). Depending on the outcome of this comprehensive analysis, PANCAID will provide the design of a future prospective study for validation of the developed composite blood test in an international multi-center setting required to introduce LBx into screening programs for high-risk individuals. This action is part of the Cancer Mission cluster of projects on ‘Prevention, including Screening’.