VASCUL-AID | DEVELOPING TRUSTWORTHY ARTIFICIAL INTELLIGENCE (AI)-DRIVEN TOOLS TO PREDICT VASCULAR DISEASE RISK AND PROGRESSION

Original description

The aim of VASCUL-AID is to predict the risk of cardiovascular events and progression of the vascular diseases Abdominal Aortic Aneurysm (AAA) and Peripheral Arterial Disease (PAD) to influence the course of disease improving the patient’s quality of life and care and assisting clinicians to make better-informed decisions involving the patient. VASCUL-AID will allow us for the first time to identify patients who are at high risk for AAA growth or PAD progression and cardiovascular events. To this end, we will deliver a clinically relevant and cost-effective trustworthy AI-driven platform (VASCUL-AID) that integrates multi-source parameters including imaging, proteomic and genomic data as well as life-style patient data from wearables to enable personalised vascular disease management. To maximise the personalised prevention strategies, VASCUL-AID leverages visualisation tools to improve clinician-patient communication and empower the patient. The VASCUL-AID platform consists of AI risk-prediction tools, a patient communication app an a clinical dashboard to support clinical decision-making. A particular emphasis is placed on ethics, to ensure beneficial implementation of AI prediction tools.
In this project, we aim to (1) build an EU-wide data infrastructure, (2) develop an AI-based progression prediction tools for AAA and PAD, (3) develop criteria according to the COMET initiative to assess the effectiveness of VASCUL-AID, and (4) clinically test and show proof-of-concept for the VASCUL-AID platform. Once validated, this platform can be extended to other cardiovascular diseases (CVDs) as well. VASCUL-AID brings together 14 leading organisations (and 2 affiliated entities) consisting of clinical academic centres, institutes, universities and SMEs as well as large industry, patients organisations and policy makers that cover the full value chain to enable integration of the platform into clinical practice.

Status

SIGNED

Url

https://cordis.europa.eu/project/id/101080947

Call topic

HORIZON-HLTH-2022-STAYHLTH-01-04-two-stage

Update Date

31-07-2023