Direct and Real-Time Cardiovascular Risk Prediction

TL;DR

This paper introduces a real-time, automatic method for quantifying coronary artery calcium in chest CT scans, which predicts cardiovascular risk accurately without requiring detailed segmentation.

Contribution

It presents a novel deep learning approach that bypasses segmentation, enabling fast and accurate CAC quantification directly from CT images.

Findings

ICC of 0.98 with manual scores

85% accuracy in risk stratification

Cohen's kappa of 0.90 for risk categories

Abstract

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 $\kappa$ 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.