Dataset related to the article "A token-mixer architecture for CAD-RADS classification of coronary stenosis on multiplanar reconstruction CT images"

This record contains raw data related to the article "A token-mixer architecture for CAD-RADS classification of coronary stenosis on multiplanar reconstruction CT images"

A B S T R A C T
Background and objective: In patients with suspected Coronary Artery Disease (CAD), the severity of stenosis needs
to be assessed for precise clinical management. An automatic deep learning-based algorithm to classify coronary
stenosis lesions according to the Coronary Artery Disease Reporting and Data System (CAD-RADS) in multiplanar
reconstruction images acquired with Coronary Computed Tomography Angiography (CCTA) is proposed.
Methods: In this retrospective study, 288 patients with suspected CAD who underwent CCTA scans were included.
To model long-range semantic information, which is needed to identify and classify stenosis with challenging
appearance, we adopted a token-mixer architecture (ConvMixer), which can learn structural relationship over
the whole coronary artery. ConvMixer consists of a patch embedding layer followed by repeated convolutional
blocks to enable the algorithm to learn long-range dependences between pixels. To visually assess ConvMixer
performance, Gradient-Weighted Class Activation Mapping (Grad-CAM) analysis was used.
Results: Experimental results using 5-fold cross-validation showed that our ConvMixer can classify significant
coronary artery stenosis (i.e., stenosis with luminal narrowing ≥50%) with accuracy and sensitivity of 87% and
90%, respectively. For CAD-RADS 0 vs. 1–2 vs. 3–4 vs. 5 classification, ConvMixer achieved accuracy and
sensitivity of 72% and 75%, respectively. Additional experiments showed that ConvMixer achieved a better
trade-off between performance and complexity compared to pyramid-shaped convolutional neural networks.
Conclusions: Our algorithm might provide clinicians