Coronary heart disease is the leading cause of mortality worldwide, accounting for 1/3 of all global deaths. Treatment of stable coronary heart disease is typically performed by medication/lifestyle for a lower disease burden or PCI (stenting) for a greater disease burden. The choice between these treatments is best determined by an invasive diagnostic test that measures blood flow through a diseased area. Unfortunately, this invasive diagnostic test is expensive, dangerous and usually finds a lower disease burden. We are working to change the diagnostics paradigm with blood flow simulation in a personalized heart model that is derived from cardiac CT angiography images. This simulation-based diagnostic is the first clinically available diagnostic that utilizes personalized simulation and is much safer and more comfortable for the patient as well as less expensive. Our diagnostic depends on a hyperaccurate image segmentation of the coronary arteries, physiological modeling and accurate computational fluid dynamics. In this talk I will discuss the algorithms that drive this technology, the machine learning that we're doing with our database of segmented images and personalized hemodynamics, and the successful clinical trials that have proven the diagnostic accuracy and benefit to patients.
Posted by: Nathan Galli