Automatic identification of patients with functionally significant obstructive coronary artery disease using non-invasive cardiac CT

Developing AI models to identify patients with functionally significant coronary artery disease.








About project

Coronary artery disease (CAD) remains the first cause of morbidity and mortality in the Western world and it is expected that this trend will continue in the coming years. In clinical routine, patients with CAD are increasingly identified using non-invasive coronary CT angiography (CCTA), a non-invasive imaging tool for detection and exclusion of the obstructive coronary artery stenosis. Despite its high sensitivity, CCTA is currently not capable of determining the functional significance of the detected stenosis. Therefore, after undergoing CCTA, many patients undergo invasive coronary angiography (ICA). In this project, we design a quantitative method to determine which coronary artery stenoses as seen on CCTA images are functionally significant, and thereby to identify patients who need to undergo invasive coronary catheterization and spare those who do not. The project is part of a collaborative research project between TU/e and UMC Utrecht funded by the Netherlands Organisation for Health Research and Development and Pie Medical Imaging.


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