Coronary artery calcification is an important and independent predictor of adverse cardiovascular events such as heart attacks. But despite this knowledge, and the fact that it can be assessed from any chest CT scan, quantification of coronary artery calcium (CAC) is not automatically integrated into the patient pathway as it requires radiological expertise, time, and specialized equipment.
To remedy this, a multidisciplinary team developed and tested a deep-learning algorithm that can automatically quantify CAC from any chest CT scan. In theory, the deep-learning system does a lot of what a human would do to quantify calcium.
To develop the algorithm, the researchers used 1636 scans from a seminal yet ongoing cardiovascular study to identify and quantify CAC, using manual segmentations performed by expert CT readers as ground truth to train the deep-learning system. The deep-learning system uses three consecutive convolutional neural networks to predict the heart center, segment the heart, and segment and identify coronary calcium in less than 2 s. It then computes the CAC scores and stratifies them into clinically relevant categories: very low (CAC=0); low (CAC=1–100); moderate (CAC=101–300); and high (CAC>300).