Classification of coronary artery disease severity based on SPECT MPI polarmap images and deep learning: A study on multi-vessel disease prediction

• Published in: Digital health 2024-01, Vol.10, p.20552076241288430
• Main Authors: Chen, Jui-Jen, Su, Ting-Yi, Huang, Chien-Che, Yang, Ta-Hsin, Chang, Yen-Hsiang, Lu, Henry Horng-Shing
• Format: Article
• Language: English
• Subjects: Cardiovascular disease; Coronary vessels; Deep learning; Health care expenditures; Original; Vein & artery diseases
• ISSN: 2055-2076; 2055-2076
• DOI: 10.1177/20552076241288430

Background Coronary artery disease (CAD) is a global health concern. Conventional single photon emission computed tomography myocardial perfusion imaging (SPECT MPI) is a noninvasive method for assessing the severity of CAD. However, it relies on manual classification by clinicians, which can lead to visual fatigue and potential errors. Deep learning techniques have displayed promising results in CAD diagnosis and prediction, providing efficient and accurate analysis of medical images. Methods In this study, we explore the application of deep learning methods for assessing the severity of CAD and identifying cases of multivessel disease (MVD). We utilized the EfficientNet-V2 model in combination with DeepSMOTE to evaluate CAD severity using SPECT MPI images. Results Utilizing a dataset consisting of 254 patients (176 with MVD and 78 with single-vessel disease [SVD]), our model achieved an accuracy rate of 84.31% and area under the receiver operating characteristic curve (AUC) value of 0.8714 in predicting cases of MVD. These results underline the promising potential of our approach in MVD prediction, offering valuable diagnostic insights and the prospect of reducing medical costs. Conclusion This study emphasizes the feasibility of employing deep learning techniques for predicting MVD based on SPECT MPI images. The integration of Efficient-Net-V2 and DeepSMOTE methods effectively evaluates CAD severity and distinguishes MVD from SVD. Our research presents a practical approach to the early prediction and diagnosis of MVD, ultimately leading to enhanced patient outcomes and reduced healthcare costs.