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| Funder | European Commission |
|---|---|
| Recipient Organization | Medizinische Universitaet Wien |
| Country | Austria |
| Start Date | May 01, 2023 |
| End Date | Apr 30, 2027 |
| Duration | 1,460 days |
| Number of Grantees | 13 |
| Roles | Coordinator; Associated Partner; Participant |
| Data Source | European Commission |
| Grant ID | 101080302 |
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.
Medizinische Universitaet Wien; Imperial College of Science Technology and Medicine; Universitatsklinikum Bonn; Ruprecht-Karls-Universitaet Heidelberg; Collective Minds Radiology Ab; Eibir Gemeinnutzige Gmbh Zur Forderung Der Erforschung Der Biomedizinischen Bildgebung; Ruhr-Universitaet Bochum; Semmelweis Egyetem; Universitat Zurich; Brightfish Bv; Medicalvalues Gmbh; Univerzita Karlova; Katholieke Universiteit Leuven
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