Abstract
Artificial intelligence (AI) is revolutionizing cardiovascular imaging, with aortic computed tomography angiography (CTA) emerging as a prominent area of application. CTA imaging is essential for the diagnosis, risk stratification, and treatment planning of aortic diseases. However, conventional CTA techniques face limitations such as radiation exposure, contrast agent risks, and reliance on manual interpretation. The integration of AI into aortic CTA offers innovative solutions across multiple domains. AI can enhance image quality, automate anatomical segmentation, improve diagnostic accuracy for aortic emergencies, and provide quantitative tools for prognostic evaluation following interventions like endovascular aortic repair. Furthermore, this review provides the analysis of emerging techniques, including advanced image synthesis methods, Vision Transformer architectures, multi-task learning, weakly supervised learning, and the paradigm shift introduced by Foundation Models, emphasizing their potential for clinical application. This work comprehensively summarizes the current applications and nascent technological paradigms of AI in aortic CTA, along with existing challenges and future research directions.