THE IMPACT OF PERCUTANEOUS CORONARY INTERVENTION USING THE NOVEL DYNAMIC CORONARY ROADMAP SYSTEM

Procedural and Technological Innovations Facilitating Ultra-low Contrast Percutaneous Coronary Interventions

Ultra-low-dose contrast percutaneous coronary intervention (PCI) is a valuable approach in selected complex high-risk patients with renal failure. One of the objectives of ultra-low contrast PCI is to decrease the probability of developing postprocedural contrast-induced nephropathy (CIN), which predominately affects patients with baseline renal dysfunction. CIN is associated with poor clinical outcomes and increased healthcare-related costs. Another two clinical scenarios in which reduced dependence on contrast administration by the operator may contribute to improved safety are PCI in complex, high-risk indicated patients and in shock. In this review, we discuss the procedural techniques and recent technological innovations that enable ultra-low-dose contrast PCI to be performed in the cardiac cath lab.

Advances in Diagnosis, Therapy, and Prognosis of Coronary Artery Disease Powered by Deep Learning Algorithms

Percutaneous coronary intervention has been a standard treatment strategy for patients with coronary artery disease with continuous ebullient progress in technology and techniques. The application of artificial intelligence and deep learning in particular is currently boosting the development of interventional solutions, improving the efficiency and objectivity of diagnosis and treatment. The ever-growing amount of data and computing power together with cutting-edge algorithms pave the way for the integration of deep learning into clinical practice, which has revolutionized the interventional workflow in imaging processing, interpretation, and navigation. This review discusses the development of deep learning algorithms and their corresponding evaluation metrics together with their clinical applications. Advanced deep learning algorithms create new opportunities for precise diagnosis and tailored treatment with a high degree of automation, reduced radiation, and enhanced risk stratification. Generalization, interpretability, and regulatory issues are remaining challenges that need to be addressed through joint efforts from multidisciplinary community.