Evaluation of myocardial perfusion imaging techniques and artificial intelligence (AI) tools in coronary artery disease (CAD) diagnosis through multi-criteria decision-making method.

Background: Cardiovascular diseases (CVDs) continue to be the world's greatest cause of death. To evaluate heart function and diagnose coronary artery disease (CAD), myocardial perfusion imaging (MPI) has become essential. Artificial intelligence (AI) methods have been incorporated into diagnostic methods such as MPI to improve patient outcomes in recent years. This study aims to employ a novel approach to examine how parameters/criteria and performance metrics affect the prioritization of selected MPI techniques and AI tools in CAD diagnosis. Identifying the most effective method in these two interconnected areas will increase the CAD diagnosis rate.

Methods: The study includes an in-depth investigation of popular convolutional neural network (CNN) models, including InceptionV3, VGG16, ResNet50, and DenseNet121, in addition to widely used machine learning (ML) models, including random forests (RF), K-nearest neighbor (KNN), support vector machine (SVM), and Naïve Bayes (NB). In addition, it includes the evaluation of nuclear MPI techniques, including positron emission tomography (PET) and single photon emission computed tomography (SPECT), with the non-nuclear MPI technique of cardiovascular magnetic resonance imaging (CMR). Various performance metrics were used to evaluate AI tools. They are F1-score, recall, specificity, precision, accuracy, and area under the receiver operating characteristic curve (AUC-ROC). For MPI techniques, the evaluation criteria include specificity, sensitivity, radiation dose, cost of scan, and study duration. The analysis was evaluated and compared using the fuzzy-based preference ranking organization method for enrichment evaluation (PROMETHEE), the multi-criteria decision-making method (MDCM).

Results: According to the study's findings, considering selected performance metrics or criteria, RF is the most efficient AI tool for SPECT MPI in the diagnosis of CAD with a net flow (Φ^net ) of 0.3778, and it's revealed that CMR is the most efficient MPI technique for CAD diagnosis with a net flow of 0.3666. By expanding this study, more comprehensive evaluations can be made in the diagnosis of CAD.

Conclusions: It was concluded that CMR outperformed the nuclear MPI techniques. SPECT, as the least advantageous technique, remained below average on other criteria except for the cost of the scan. Integrating the RF algorithm, which stands out as the most effective AI tool in diagnosing CAD, with SPECT MPI may contribute to SPECT becoming a superior alternative.