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Updated: Jan 3, 2026
Author Spotlight: Advancing Cardiovascular Imaging - Introducing the Spatially Weighted Calcium Score for Early Disease Detection
Published on: September 22, 2023
Coronary Calcium Detection using 3D Attention Identical Dual Deep Network Based on Weakly Supervised Learning
Yuankai Huo1, James G Terry2, Jiachen Wang1
1Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, USA.
Insights
Coronary artery calcium (CAC) detection is automated using a novel deep learning model, AID-Net, on CT scans. This approach offers a faster, more efficient method for identifying early signs of heart disease.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Cardiology
Background:
- Coronary artery calcium (CAC) indicates advanced subclinical coronary artery disease, predicting future cardiovascular events.
- Manual CAC detection from CT scans is the gold standard but is labor-intensive and impractical for large populations.
Purpose of the Study:
- To develop an automated deep learning model for efficient and accurate CAC detection.
- To utilize weakly supervised attention mechanisms on longitudinal non-contrast CT scans for CAC identification.
Main Methods:
- Proposed the attention identical dual network (AID-Net) incorporating 3D attention mechanisms for enhanced classification.
- Employed weakly supervised learning using only per-scan labels, reducing annotation burden.
- Integrated 3D Gradient-weighted Class Activation Mapping (Grad-CAM) for model interpretability.
Main Results:
- The AID-Net achieved a classification accuracy of 0.9272 and an AUC of 0.9627 on a dataset of 5075 non-contrast chest CT scans.
- Demonstrated superior performance compared to baseline methods.
- The model effectively leverages longitudinal scan data for improved detection.
Conclusions:
- The AID-Net provides a highly accurate and efficient automated solution for CAC detection.
- This deep learning approach facilitates large-scale screening for subclinical coronary artery disease.
- The interpretability of the model aids in clinical trust and understanding.
Abstract:
Coronary artery calcium (CAC) is biomarker of advanced subclinical coronary artery disease and predicts myocardial infarction and death prior to age 60 years. The slice-wise manual delineation has been regarded as the gold standard of coronary calcium detection. However, manual efforts are time and resource consuming and even impracticable to be applied on large-scale cohorts. In this paper, we propose the attention identical dual network (AID-Net) to perform CAC detection using scan-rescan longitudinal non-contrast CT scans with weakly supervised attention by only using per scan level labels. To leverage the performance, 3D attention mechanisms were integrated into the AID-Net to provide complementary information for classification tasks. Moreover, the 3D Gradient-weighted Class Activation Mapping (Grad-CAM) was also proposed at the testing stage to interpret the behaviors of the deep neural network. 5075 non-contrast chest CT scans were used as training, validation and testing datasets. Baseline performance was assessed on the same cohort. From the results, the proposed AID-Net achieved the superior performance on classification accuracy (0.9272) and AUC (0.9627).
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