## 1 Introduction
### Related Work
It is worth noting that the DL model, when used independently, achieved an AUC of 0.793, showcasing its potential in improving the diagnostic performance for CAD. Incorporating the explainable DL model provided higher accuracy and facilitated the interpretation of the results, making it a valuable tool for assisting medical professionals in CAD diagnosis and decision-making. [PERSON] et al. [22; 23] introduced an explainable DL model to detect obstructive CAD. Their study included a large dataset of 3578 patients with suspected CAD from nine different centres. The authors proposed a hand-crafted CNN to process SPECT Polar Maps acquired under stress conditions. In the fully connected layer of the CNN, they incorporated additional features such as the patient's sex and age to augment the model's input. When evaluated against the invasive coronary angiography findings, this method achieved an impressive AUC score of 0.83 through a 10-fold cross-validation procedure, outperforming the quantitative analysis results by expert readers (AUC = 0.8). Moreover, attention maps were generated to highlight the regions and segments that contributed the most to the per-vessel predictions, providing insights into the model's decision-making process and making it more interpretable for clinical application. [PERSON] et al. [24] developed an explainable deep learning model for predicting nonfatal myocardial infarction (MI) or death, highlighting image regions relevant to obstructive CAD. The study included 20,401 patients who underwent SPECT MPI procedures for training and internal testing, and an additional 9019 patients were included in the external testing group from two different sites to assess the generalisability. The dataset comprised stress and rest polar maps, age, sex, and cardiac volumes, which were added at the first fully connected layer. To enhance the explainability, the researchers developed Grad-CAM. For comparison, a logistic regression model was also developed using age, sex, stress TPD, rest TPD, stress left ventricular ejection fraction, and stress left ventricular end-systolic volume as the input features. The developed deep learning model achieved an impressive AUC of 0.76, outperforming stress TPD with an AUC of 0.63 and ischemic TPD with an AUC of 0.6. Moreover, it also improved upon the logistic regression model, which achieved an AUC of 0.72. The explainable deep learning model provided enhanced accuracy compared to traditional quantitative approaches and exhibited good calibration and robust results. These findings indicated the potential of the developed model for improved risk prediction and decision-making in the context of myocardial infarction and CAD. [PERSON] et al. [25] examined 979 SPECT subjects from a local hospital for diagnosis; however, it was not specified whether the images were labelled based on experts' visual inspections or ICA findings. A three-dimensional CNN was employed to classify the SPECT slices, and Grad-CAM heat maps were generated to identify myocardial defects in the images. The proposed model achieved impressive accuracy, sensitivity, and specificity metrics of 87.64%, 81.58%, and 92.16% in distinguishing between normal and abnormal images using a test set of 89 samples. These results demonstrated the model's promising capabilities for the accurate and reliable classification of SPECT images to diagnose cardiovascular conditions. [PERSON] et al. [26] explored using Graph Convolutional Neural Networks (Graph CNNs) for diagnosing CAD. They included 946 polar map images representing the stress and rest conditions of the heart and labelled them based on human observer interpretations. Heatmaps were generated to highlight the pathological segments of the heart. The results demonstrated the model's strong performance in classifying unseen data during a four-fold cross-validation procedure, outperforming the clinical visual analysis, with 92.8% specificity for the rest data and 95.9% for the stress data.