Intravascular imaging of coronary artery: Bridging the gap between clinical needs and technical advances

Abstract

Coronary artery disease is the leading cause of mortality worldwide. Almost seven million deaths are reported each year due to coronary disease. Coronary artery events in the adult are primarily due to atherosclerosis with seventy-five percent of the related mortality caused by plaque rupture. Despite significant progress made to improve intravascular imaging of coronary arteries, there is still a large gap between clinical needs and technical developments. The goal of this review is to identify the gap elements between clinical knowledge and recent advances in the domain of medical image analysis. Efficient image analysis computational models should be designed with respect to the exact clinical needs, and detailed features of the tissues under review. In this review, we discuss the detailed clinical features of the intracoronary plaques for mathematical and biomedical researchers. We emphasize the importance of integrating this clinical knowledge validated by clinicians to investigate the potentially effective models for proper features efficiency in the scope of leveraging the state-of-the-art of coronary image analyses.

Keywords: Atherosclerotic plaque; Coronary artery disease; Deep learning; Medical image analysis; Optical coherence tomography.