Anatomical and Functional Assessment of Coronary Artery Disease Using Machine Learning

The current gold standard for Coronary Artery Disease (CAD) diagnosis is X-ray angiography. Visual estimation can be subjective and overestimate disease severity, therefore semi-automated software tools such as Quantitative Coronary Angiography (QCA) have been developed to quantify disease severity. Alternatively, functional metrics such as Fractional Flow Reserve (FFR) have demonstrated to lead to better diagnostic outcomes than anatomical assessment, but they are not widely used due to cost and risk. Ideally, quantitative and functional information could be derived directly from X-ray angiography images without the additional risks, time, and cost associated with performing FFR or QCA.

The goal of this project is to develop automated data-driven approaches to anatomical and functional quantification of disease severity using X-ray angiography images. To this end, we have developed algorithms for 1) automated coronary vessel segmentation, 2) stenosis detection and characterization, 3) 3D reconstruction of coronary anatomy, 4) image-based flow extraction, and 5) Reduced-order modeling of FFR. These algorithms can be used in conjunction with computational fluid dynamics (CFD) modeling to assess the functional significance of disease.

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