Caitlyn Gilbert et al. Alec Chunta Robert Miller
Abstract
Recent advancements in artificial intelligence (AI) have significantly enhanced the efficiency and quality of nuclear cardiology imaging. AI algorithms, such as generative adversarial networks (GAN) and deep learning (DL) models, are improving image acquisition by reducing noise and correcting motion to minimize radiation exposure while maintaining diagnostic accuracy. AI has also demonstrated the potential to create synthetic attenuation correction (AC) imaging, accounting for soft-tissue attenuation without added computed tomography (CT) imaging. In patients with CT attenuation imaging, AI can automate the segmentation of coronary artery calcification (CAC), epicardial adipose tissue (EAT), and other structures to maximize the clinical information obtained from these added studies. Furthermore, machine learning (ML) models that combine clinical, imaging, and stress test data are advancing risk prediction for coronary artery disease (CAD) and ischemia, assisting clinicians in decision-making and improving patient outcomes. While many of these applications have been developed using myocardial perfusion imaging (MPI), they are increasingly applied to other nuclear cardiology studies. This review explores these AI applications and their impact on diagnostic accuracy, workflow efficiency, and risk stratification while also highlighting potential future directions and challenges.