Heart failure (HF) is a pandemic currently affecting up to 15 million people in Europe. It is a complex clinical syndrome presenting with impaired heart function and is associated with poor quality of life for patients and high healthcare costs. 

There is a high clinical demand for novel artificial intelligence (AI) tools that will facilitate risk stratification, early diagnosis, and disease progression assessment in HF. Such tools are essential to allow prompt initiation of evidence-based prevention and treatment strategies which will improve patient quality of life, reduce morbidity and mortality and the HF burden on healthcare. 

STRATIFYHF aims to develop, validate and implement the first AI-based, decision support system (DSS) for risk stratification, early diagnosis, and disease progression assessment in HF to accommodate both primary and secondary care clinical needs. The DSS will integrate patient-specific demographic and clinical data using existing and novel technologies and establish AI-based tools for risk stratification and HF prediction using machine learning.

Additionally, a mobile app will be developed to empower patients to better manage their condition, and healthcare professionals to make informed decision in selection of evidence-based HF prevention and treatment strategies. 

Our multidisciplinary consortium, including three small-to-medium enterprises(SMEs) and two stakeholder organisations, will be guided by medical advice and regulatory and health technology experts to deliver the DSS as a medical class 2b device, reaching TRL 8 by the end of the project. 

STARTIFYHF will change the way in which HF is diagnosed today and thereby improve the quality and length of patients’ lives and lead to efficient and sustainable healthcare systems by reducing the number of HF-related hospital admissions and unnecessary referrals from primary to secondary care in Europe and beyond.

Psychotic disorders tend to have a waxing and waning course, with vulnerable persons being at risk for a relapse, especially when antipsychotic medication is no longer used. Such psychotic relapses can often be prevented, when a warning is received in time, so that the patient and their clinical team can take appropriate measures. 

Using artificial intelligence, we will analyse these speech fragments with the aim to recognise changes in speech. These changes may signal subtle tendencies of psychotic symptoms, such as hallucinations, delusions or thought disorder.

These can consist of the reinstatement of medication, psychotherapy, social interventions or a combination of those. Accurate and timely warning can thus make a huge difference. We hypothesise that spontaneous speech specifically holds the key to predict whether psychosis is imminent. In this project, we will ask people at risk of psychotic relapse to record short speech samples at regular intervals through a private and safe (remote) electronic environment. 

Our goal is to develop a user-friendly, trustworthy application that can be used from home to deliver a message when speech deviations predictive of psychotic relapse are detected. We hope that such an application will support people vulnerable for psychosis to live independently without fear of relapse with or without antipsychotic maintenance medication.

The ARISTOTELES project aims to build a multinational harmonized data platform to develop and implement novel artificial intelligence (AI) approaches for management of complex diseases, where progression and manifestations of comorbidities are via multiple interacting pathways. We aim to apply our novel approach to a population of great need due to atrial fibrillation (AF), but our outputs can be extended to other complex diseases with multimorbidity. 

By integrating AIs into clinical practice, our platform will form a backbone for acceptable, responsible, and respectful uses of patient/participant data to develop and validate novel trustworthy AI tools for more personalized risk assessment and management. 

This represents a paradigm shift in AF treatment, moving from a focus on individual risk factors and selected outcomes (eg. stroke) to a holistic approach, underpinning timely diagnostic and therapeutic interventions to reduce disease progression, disability, hospitalizations and mortality, as well as improve patient adherence to lifestyle modifications, medications, and other treatment regimens.

The AI-POD project is set to tackle the significant issue of obesity and related heart diseases across Europe, using the power of artificial intelligence (AI). With more than 436 million people in Europe dealing with weight problems, there is a clear and urgent need for a solution. Obese individuals are 50% more likely to die from heart diseases, and the cost of treating these conditions is a massive 210 billion Euros each year. 

Today, one of the biggest challenges is figuring out who among the obese population is most at risk of severe heart disease events. The methods we currently have aren't precise enough in predicting this, and there aren't any easy to use tools that apply these methods to help people manage their health.

That's where the AI-POD project comes in. The ultimate goal is to reduce the number of people suffering from heart diseases in Europe. To do this, the project aims to create an AI based score that can predict a person's risk of develop ing serious heart disease. This score will support doctors in making important decisions about patient care.

The project will develop AI tools that can be trusted by the people who need them most those struggling with obesity. These tools will use a mix of clinical, lab, and imaging data to turn disease risk into understandable and actionable health information. This can guide the steps needed for further diagnosis and treatment. All these AI tools will be tested in six clinical sites on heart disease patients.

In summary, the AI-POD project will push the limits of our understanding and management of heart disease in obese individuals. The two main outcomes of this project will be an AI based risk score and a clinical decision support system, which will he lp doctors assess and predict heart disease risks and complications. We will also develop an easy to use mobile app for citizens, that interacts with this system, empowering individuals to better monitor and manage their health.

The project aims to make life better for doctors and patients by streamlining workflows, and to reduce the strain on public health budgets by decreasing the number of obese individuals suffering from heart disease.

The World Health Organization (WHO) estimates that more than 70% of deaths worldwide, including up to 90% of deaths in the European region, are due to non-communicable chronic diseases (NCDs). Most of these diseases share predisposing risk factors such as obesity and low levels of physical fitness resulting from unhealthy lifestyle (including insufficient physical activity, prolonged time spent in sedentary pursuits, poor nutrition, inappropriate sleep duration, cigarette smoking and abusive alcohol consumption).

Notwithstanding major improvements in the treatment of NCDs, primary prevention strategies that target healthy individuals are a more effective solution compared to prevention of adverse outcomes at early stages of the disease or treating a fully developed disease. Additionally, even though biological risk factors usually emerge in adulthood, childhood and adolescents are the ideal period for risk-lowering strategies based on behaviour changes.

Given that 80% of parents of inactive children wrongly consider their children to be sufficiently active, that existing risk calculation tools are used on adults, and that there is very little understanding about the appropriate level of specific behaviours even among health professionals (who are forced to rely on imperfect tools such as BMI), there is a deep need for tools to fight back against NCDs more effectively.

This is where SmartCHANGE comes in: the project's goal is to develop trustworthy, AI-based decision-support tools that will help health professionals and citizens reduce long-term risk of NCDs by accurately assessing the risk of children and teens and promoting optimised risk-lowering strategies.

By engaging users right from the start of the application's development and by applying machine learning to ethical datasets, the project will revolutionise health monitoring and wellness encouragement for youth, as well as preventing their risks of contracting diseases later in life.

We are dedicated to revolutionizing the management of vascular diseases, specifically Abdominal Aortic Aneurysm (AAA) and Peripheral Arterial Disease (PAD). Our mission is to predict the risk of cardiovascular events and disease progression, ultimately improving the quality of life and care for patients.

For the first time, VASCUL-AID offers a groundbreaking solution to identify patients who are at high risk for AAA growth or PAD progression, enabling early intervention and personalized prevention strategies. Our cutting-edge AI-driven platform integrates a wide range of data, including imaging, proteomic and genomic data, as well as lifestyle information from wearable devices. This comprehensive approach allows us to deliver clinically relevant and cost-effective predictions, supporting both patients and clinicians in making better-informed decisions.

Breast cancer is the most common cancer in women globally. Rising incidence and improvements in cancer care have contributed to a growing number of breast cancer survivors. As these cancer survivors live longer, and with almost 80% of them being older than 50 years of age, they are at higher risk of developing other chronic diseases and conditions like cardiovascular disease (CVD), weight gain, and osteoporosis.

The risk of chronic diseases is higher women with breast cancer than that of the general population, which is partly explained by treatment induced toxicity (eg. chemotherapy induced cardiotoxicity, radiation induced cardiotoxicity and lung fibrosis) and side effects (eg. hormone therapy induced osteoporosis, systemic therapy related weight gain), and partly by shared risk factors that predispose for breast cancer and other conditions like overweight and less physical exercise. Overall, development of chronic diseases after a breast cancer diagnosis has an unfavorable impact on quality of life and survival.

Most breast cancer patients (60-65%) are treated with radiotherapy. Planning computed tomography (CT)-images are obtained for delineation and computation of radiation dose distribution fields. These CT-images contain information on risk factors for other diseases like CVD (eg. calcifications in the epicardial coronary arteries and aorta) and early signs of osteoporosis. This potentially valuable but unrequested information is currently not systematically assessed nor reported by professionals, largely due to time constraints and unfamiliarity with its potential importance.

In the ARTILLERY project funded by Horizon Europe, we aim to develop, validate, and prospectively evaluate AI systems for automated early detection of chronic disease risk (factors) in women with breast cancer by using routine CT-images.

The “Trustworthy Artificial Intelligence for Personalised Risk Assessment in Chronic Heart Failure (AI4HF)” project is an innovative initiative that harnesses the power of Artificial Intelligence (AI) to provide personalized risk assessment and care plans for individuals living with Chronic Heart Failure. It utilizes advanced AI algorithms, global collaboration, and a patient-centered approach to improve healthcare outcomes. This four-year project is led by prof. dr. F.W. (Folkert) Asselbergs, coordinated by the Netherlands Heart Institute, and being conducted by a consortium of 16 international (associated) partners.

Trustroke, an European artificial intelligence project aimed to optimise stroke treatment.

Assists in managing stroke patients and assessing disease progression using clinical data, focusing on:

• Clinical worsening leading to unplanned hospital readmissions.

• Poor mobility, incomplete recovery and unfavorable clinical long-term outcomes.

• Stroke recurrence.