### Machine Learning and Deep Learning in SPECT Nuclear Cardiology Imaging  
Currently, regarding SPECT MPI, which is one of the established methods for imaging in nuclear cardiology, researchers face the challenge of developing an algorithm that can automatically characterize the status of the patients with known or suspected coronary artery disease. The accuracy of this algorithm needs to be extreme due to the importance of people's lives. Since deep learning algorithms have the capacity to improve the accuracy of CAD screening, they have been broadly explored in the domain of nuclear cardiovascular imaging analysis. ML and DL methods have both been explored to assess the likelihood of obstructive CAD. In the context of ML algorithms for CAD diagnosis, ANN, SVM and boosted ensemble methods have been investigated. In a single-center study for the detection of obstructive CAD, ML was utilized with SPECT myocardial perfusion imaging (MPI) combining clinical data of 1181 patients and provided AUC values (0.94 \(\pm\) 0.01), which were significantly better than total perfusion deficit (0.88 \(\pm\) 0.01) or visual readout [44]. ML was also explored in the multi-center REFINE SPECT (Registry of Fast Myocardial Perfusion Imaging with NExt generation SPECT) registry [45]. In this study, 1980 patients of possible CAD went through a stress/rest 99 mTc-secstamibi/tetrofosmin MPI. The ML algorithm embedding 18 clinical, 9 stress test, and 28 imaging variables from 1980 patients produced an AUC of 0.79 [0.77, 0.80], which is higher than that regarding (TPD) 0.71 [0.70, 0.73] or ischemic TPD 0.72 [0.71, 0.74] in the prediction of early coronary revascularization. In [46], ANN was applied for interpreting MPS with suspected myocardial ischemia and infarction on 418 patients who underwent ECG-gated MPS at a single hospital. The ANN-based method was compared against a conventional automated quantification software package. The results showed that the model based on neural networks presents interpretations more similar to experienced clinicians than the other method examined. Using clinical and other quantification data, the authors of [47] deployed the boosted ensemble machine learning algorithm and the ANN, achieving classification accuracy of up to 90%. SVMs have been exploited in [48] and have been trained considering a group of 957 patients with either correlating invasive coronary angiography or a low possibility of CAD. The AUC value produced for SVM classifier combining quantitative perfusion (TPD and ISCH) and functional data was as high as 86%. Moreover, several recent research studies explore ML and DL methods for diagnosing CAD in nuclear cardiology using polar maps instead of SPECT MPI scans. The studies devoted to polar maps are set out as follows: in [49], Perlex and an ANN are used with polar maps, while in [50; 51; 52], polar maps were utilized along with DL methods. In [49], polar maps of stress and rest examinations of 243 patients who underwent SPECT and coronary angiography within three months were used as input images to train ANN models. The produced AUC results of receiver operating characteristics (ROC) analysis for neural networks was 0.74, surpassing the corresponding AUC for other physicians. Regarding the application of DL for CAD prediction, the authors in [50] employed deep learning, which was trained using polar maps, for predicting obstructive disease from myocardial perfusion imaging (MPI). The outcome is an improved automatic interpretation of MPI comparing to the total perfusion deficit (TPD). As a result, a pseudo-probability of CAD was deployed per vessel region and per individual patient. An AUC value of 0.80 was calculated concerning the detection of 70% stenosis or higher, still outperforming TPD. The DL procedure automatically predicted CAD from 2-view (upright and supine) polar maps data obtained from dedicated cardiac scanners in 1160 patients, improving current perfusion analysis in the prediction of obstructive CAD [50].