Project

Integrating Medical Image Data and Assessments for Personalized Cardiovacular Risk Estimation

Short Summary

Cardiovascular disease is one of the leading causes of death in Switzerland. However up to 17% of those who develop cardiovascular disease are assigned to low risk category by current risk calculators. Therefore, improving diagnosis, using abundant biomarker, medical image and genetics data will greatly facilitate more accurate estimation of cardiovascular disease risk. In this project we will apply deep learning methods to integrate all data modalities to create more accurate and timely risk estimator.

Goals

The goal of this proposal is to use full spectrum of data: novel risk factors, medical image and genetics data in order to improve accuracy of cardiovascular risk estimation.

Significance

This project will facilitate accurate and early estimation of cardiovascular disease risk which is vital for implementing timely preventive strategies that can reduce the incidence of disease and improve long-term health outcomes.

Background

Atherosclerotic Cardiovascular Disease (ASCVD) is the leading cause of death worldwide. It represents 31% of all deaths and 16% of healthcare costs in Switzerland. Enhancing diagnostic tools for early detection is crucial for prevention. Recent findings, such as the coronary artery calcification (CAC) score and arterial stiffness index, have emerged as key predictive factors for ASCVD. A CAC score above 100 notably increases ASCVD risk, yet treatments to prevent calcification are still unknown. Improved access to these indicators could advance our understanding of ASCVD and hasten the development of preventive treatments. Concurrently, AI analysis of medical images is proving to be a breakthrough in realizing the full potential of these diagnostic tools.

Data-Intensive Research Project

Co-Investigators

Prof. Dr. med Arnold von Eckarsdtein, Institut für Klinische Chemie, Universitätsspital Zürich
Prof. Dr. med Hatem Alkadhi, Institute of Diagnostic and Interventional Radiology, University Hospital Zurich
Prof. Dr med Samia Mora, Division of Preventive Medicine, Brigham and Women’s Hospital/Harvard Medical School, Boston, USA

Consortium

Brigham and Women’s Hospital

Status

In Progress

2nd Joint SDSC Call, Data-Intensive Research