Earlier this month, I attended the European Society of Cardiology (ESC) World Congress in Paris. This organization reviews evidence and publishes clinical guidelines that are respected around the world.
Following the annual meeting, ESC published new guidelines on the diagnosis and management of chronic coronary syndromes
. One of the major new recommendations made by ESC is that Coronary Computed Tomography Angiogram (CCTA) scans are to be used for diagnosing CAD in symptomatic patients in whom obstructive CAD cannot be excluded by clinical assessment alone. This follows the inclusion of coronary artery calcium (CAC) scoring in the 2019 ACC/AHA Guideline on the Primary Prevention of Cardiovascular Disease
announced at the Society for Coronary Computed Tomography in November 2018. Both of these guideline changes were, in part, supported by a 2018 study published in the Journal of the American College of Cardiology (JACC) that found that statins provide no clinical benefit
for patients when their coronary artery calcium (CAC) scores are zero.
CAC is just one of the valuable metrics that can be calculated from the digital files created by CCTA scans. We are excited about other diagnostic measurements made possible by applying machine learning to the data created during CCTA scans, including coronary artery tree reconstruction and stenosis quantification, plaque characterization, and hemodynamic modeling. The digitization of medical images and the rapid advancements in machine learning provide new opportunities for patient education. Informed consent by patients allowing use of their image data for research purposes is also a tool for engagement.
There are many more opportunities for advanced analytics where machine learning can be applied to the post-processing of diagnostic images for more personalized, precise diagnostics that can lead to better outcomes. The collaboration of medical professionals, computer scientists, and patients is required to accelerate progress. At CuraCloud, we are working with clinicians and scientists to create improved cardiovascular diagnostics that inform crucial clinical decisions.