Artificial intelligence empowered coronary artery imaging: A review

Cardiovascular disease is the leading cause of death worldwide, with coronary artery disease the most prevalent cause. Although artificial intelligence has advanced medical imaging, few reviews focus specifically on AI-powered coronary artery imaging. This review provides a systematic and critical analysis of AI applications in coronary artery imaging across three domains: measurement, including centerline extraction, vessel segmentation, and 3D reconstruction; functional assessment, including fractional flow reserve, wall shear stress, and myocardial perfusion; and disease diagnosis. Web of Science, Google Scholar, and MEDLINE were searched for 2016-2025. From 9950 records, 90 studies were selected after duplicate removal, title and abstract screening, and full-text eligibility assessment using predefined criteria and quality appraisal. Seven imaging modalities are covered: computed tomography, magnetic resonance imaging, nuclear imaging, digital subtraction angiography, intravascular ultrasound, optical coherence tomography, and synthetic data. Reported performance varies by modality, task, and dataset. Challenges for clinical implementation include dataset generalizability, computational requirements, interoperability, and explainable AI. The review discusses emerging technologies such as multimodal fusion, self-supervised learning, federated learning, and foundation models, and acknowledges current limitations. It aims to inform researchers and clinicians and support future translation of AI in coronary artery imaging.