Direct and Real-Time Cardiovascular Risk Prediction
This enables real-time cardiovascular risk prediction for patients undergoing chest CT scans, though it is incremental as it builds on existing CAC quantification methods by avoiding segmentation.
The authors tackled the problem of automating coronary artery calcium (CAC) quantification from low-dose chest CT scans to predict cardiovascular risk, achieving an ICC of 0.98 for Agatston scores and 85% accuracy in risk stratification.
Coronary artery calcium (CAC) burden quantified in low-dose chest CT is a predictor of cardiovascular events. We propose an automatic method for CAC quantification, circumventing intermediate segmentation of CAC. The method determines a bounding box around the heart using a ConvNet for localization. Subsequently, a dedicated ConvNet analyzes axial slices within the bounding boxes to determine CAC quantity by regression. A dataset of 1,546 baseline CT scans was used from the National Lung Screening Trial with manually identified CAC. The method achieved an ICC of 0.98 between manual reference and automatically obtained Agatston scores. Stratification of subjects into five cardiovascular risk categories resulted in an accuracy of 85\% and Cohen's linearly weighted $κ$ of 0.90. The results demonstrate that real-time quantification of CAC burden in chest CT without the need for segmentation of CAC is possible.