Coronary angiography: a review of the state of the art and the evolution of angiography in cardio therapeutics - Gurav, Aishwarya; Revaiah, Pruthvi C.; Tsai, Tsung-Ying; Miyashita, Kotaro; Tobe, Akihiro; Oshima, Asahi; Sevestre, Emelyne; Garg, Scot; Aben, Jean-Paul; Reiber, Johan H. C.; Morel, Marie Angele; Lee, Cheol Whan; et al. (Frontiers in Cardiovascular Medicine, 2024)
Traditionally coronary angiography was restricted to visual estimation of contrast-filled lumen in coronary obstructive diseases. Over the decades considerable development has been made in quantitatively analyzing coronary angiography, significantly improving its accuracy and reproducibility. Significant advances have been made to improve its accuracy and reproducibility. Notably, the integration of artificial intelligence (AI) and machine learning into quantitative coronary angiography (QCA) holds promise for further enhancing diagnostic accuracy and predictive capabilities. Additionally, noninvasive fractional flow reserve (FFR) indices, including CT-FFR, have emerged as valuable tools, offering precise physiological assessment of coronary artery disease without the need for invasive procedures. These innovations allow for a more comprehensive evaluation of disease severity and aid in guiding revascularization decisions. This review traces the development of QCA technologies over the years, highlighting key milestones and current advancements.It also explores prospects that could revolutionize the field, such as AI integration and improved imaging techniques. By addressing both historical context and future directions, the article underscores the ongoing evolution of QCA and its critical role in the accurate assessment and management of coronary artery diseases. Through continuous innovation, QCA is poised to remain at the forefront of cardiovascular diagnostics, offering clinicians invaluable tools for improving patient care.