Figure 3.

Otaki et al. trained a CAD-DL model: the raw myocardial perfusion, wall motion, and thickening images are input to the deep learning model “CAD-DL”

Remarkably, the investigators generated a “CAD probability map” based on gradient-weighted class activation mapping (Grad-CAM). For example, the CAD probability map in the figure indicates a high likelihood of CAD, particularly in the inferior and proximal lateral walls, which contribute to the prediction. This finding suggests a significant probability of obstruction in the right coronary artery, potentially with a gyratory branch.