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

2025-11-10

AI Summary

Coronary artery calcium (CAC) scoring currently relies on labor-intensive, semi-automatic manual analysis of non-contrast cardiac CT, resulting in substantial time consumption and poor inter-observer reproducibility. Method: We propose the first deep learning–based six-class classification model that directly predicts standardized clinical CAC score categories (Agatston 0, 1–10, 11–100, 101–400, 401–1000, >1000) from non-contrast cardiac CT images. A convolutional neural network (CNN) was trained and validated end-to-end on retrospective CT data. Contribution/Results: Our model achieves 96.5% overall accuracy and a Cohen’s kappa of 0.962 on an independent test set—demonstrating near-perfect agreement with expert annotations. It is the first to map deep learning predictions directly onto the six-tier clinical CAC grading system and enables fully automated, end-to-end CAC categorization. The model exhibits strong generalizability and clinical applicability, significantly advancing standardization, efficiency, and scalability of CAC assessment in routine practice.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Computer Vision: Object Detection & Categorization
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data

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.

Problem

• Automating coronary artery calcium scoring from cardiac CT images
• Reducing time-intensive manual analysis by radiologists
• Classifying calcium severity into six clinical categories automatically

Innovation

• Deep learning CNN model classifies calcium scores
• Automates six-category CAC scoring from CT images
• Achieves high accuracy comparable to semiautomatic methods