Predicting Coronary Artery Calcium Severity based on Non-Contrast Cardiac CT images using Deep Learning

TL;DR

This study develops a deep learning CNN model to classify coronary artery calcium scores from non-contrast cardiac CT images into six categories, achieving high accuracy and potential to streamline risk assessment.

Contribution

The paper introduces a CNN-based approach for automated six-class CAC scoring from cardiac CT images, demonstrating high accuracy and generalisability compared to semiautomatic methods.

Findings

Overall accuracy of 96.5%

High agreement with semiautomatic scoring (Cohen's kappa 0.962)

Model effectively stratifies calcium scores into six categories

Abstract

Cardiovascular disease causes high rates of mortality worldwide. Coronary artery calcium (CAC) scoring is a powerful tool to stratify the risk of atherosclerotic cardiovascular disease. Current scoring practices require time-intensive semiautomatic analysis of cardiac computed tomography by radiologists and trained radiographers. The purpose of this study is to develop a deep learning convolutional neural networks (CNN) model to classify the calcium score in cardiac, non-contrast computed tomography images into one of six clinical categories. A total of 68 patient scans were retrospectively obtained together with their respective reported semiautomatic calcium score using an ECG-gated GE Discovery 570 Cardiac SPECT/CT camera. The dataset was divided into training, validation and test sets. Using the semiautomatic CAC score as the reference label, the model demonstrated high performance on a six-class CAC scoring categorisation task. Of the scans analysed, the model misclassified 32 cases, tending towards overestimating the CAC in 26 out of 32 misclassifications. Overall, the model showed high agreement (Cohen's kappa of 0.962), an overall accuracy of 96.5% and high generalisability. The results suggest that the model outputs were accurate and consistent with current semiautomatic practice, with good generalisability to test data. The model demonstrates the viability of a CNN model to stratify the calcium score into an expanded set of six clinical categories.