Deep learning for automated exclusion of cardiac CT examinations negative for coronary artery calcium

Abstract

Coronary artery calcium (CAC) score has shown to be an accurate predictor of future cardiovascular events. Early detection by CAC scoring might reduce the number of deaths by cardiovascular disease (CVD). Automatically excluding scans which test negative for CAC could significantly reduce the workload of radiologists. We propose an algorithm that both excludes negative scans and segments the CAC. The training and internal validation data were collected from the ROBINSCA study. The external validation data were collected from the ImaLife study. Both contain annotated low-dose non-contrast cardiac CT scans. 60 scans of participants were used for training and 2 sets of 50 CT scans of participants without CAC and 50 CT scans of participants with an Agatston score between 10 and 20 were collected for both internal and external validation. The effect of dilated convolutional layers was tested by using 2 CNN architectures. We used the patient-level accuracy as metric for assessing the accuracy of our pipeline for detection of CAC and the Dice coefficient score as metric for the segmentation of CAC. Of the 50 negative cases in the internal and external validation set, 62 % and 86 % were classified correctly, respectively. There were no false negative predictions. For the segmentation task, Dice Coefficient scores of 0.63 and 0.84 were achieved for the internal and external validation datasets, respectively. Our algorithm excluded 86 % of all scans without CAC. Radiologists might need to spend less time on participants without CAC and could spend more time on participants that need their attention.