Imagine a smart health assistant that doesn't just look at your weight, but combines your medical scans and daily habits to predict heart risks. It's like moving from a generic weather forecast to a precise GPS for your heart health. This system helps doctors catch problems early and gives patients a simple app to manage their lifestyle in real-time.
Current cardiovascular risk scores are inaccurate for obese patients because they ignore real-time lifestyle data and complex plaque composition. This leads to poor prediction of heart disease and high healthcare costs.
An AI-based Clinical Decision Support System (CDSS) for risk scoring and a companion mobile app for citizen health monitoring.
If you are a health-tech app developer dealing with low user engagement in chronic disease management — this project developed a Citizen App that interacts with a clinical decision system to provide actionable health information. This allows users to monitor their own health and lifestyle in real-time.
If you are a private cardiology clinic dealing with inaccurate risk scores for obese patients — this project developed an AI-based Clinical Decision Support System (CDSS). It integrates imaging and lab data to provide more accurate risk assessments than standard BMI or Framingham scores.
If you are an AI diagnostic software company dealing with the lack of specialized tools for obese populations — this project developed an imaging-based AI risk score. This allows for better detection of plaque composition and progression in patients with obesity.
Based on available project data, no specific pricing or cost models for the final tools are mentioned.
Yes, the project aims to serve as a basis for a lasting interdisciplinary platform for distributed learning in other vascular territories.
Based on available project data, specific licensing terms or patent filings are not listed.
The project builds a Clinical Decision Support System (CDSS) designed to make physician workflows more efficient by translating complex data into actionable health information.
The project period runs from 2023-05-01 to 2027-04-30, suggesting the tools will be fully validated by April 2027.
The consortium is heavily research-driven with 9 universities and 1 research institute, but maintains a 23% industry ratio with 3 industrial partners, including 2 SMEs. This balance suggests a strong scientific foundation with a clear path toward commercial application across 9 different countries.
Contact the Medizinische Universitaet Wien for technical specifications of the CDSS.
Contact us to connect with the AI-POD consortium for early access to the risk score API.
