Coronary microvascular dysfunction (CMD) affects the small blood vessels in the heart and can lead to serious health consequences. As of now, its diagnosis relies solely on invasive testing. While Cardiac CT is frequently utilized to evaluate chest pain, its inability to measure microvascular function often results in missed CMD diagnoses.
To address this gap, our project aims to combine machine learning, clinical data, and Cardiac CT indices to develop an integrated CMD score. This innovative approach promises to enhance the precision of CMD diagnosis. Furthermore, we're exploring the potential of SGLT2-inhibitors — currently used for diabetes treatment — to positively impact CMD physiology.
Last updated12 March 2024