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

• Published in: Medical engineering & physics 2021-10, Vol.96, p.71-80
• Main Authors: Abdolmanafi, Atefeh, Duong, Luc, Ibrahim, Ragui, Dahdah, Nagib
• Format: Article
• Language: English
• Subjects: Atherosclerotic plaque; Coronary artery disease; Deep learning; Medical image analysis; Optical coherence tomography
• ISSN: 1350-4533; 1873-4030
• DOI: 10.1016/j.medengphy.2021.09.003

•Atherosclerosis is a lipid-driven and chronic coronary inflammatory disease that induces stiffening of the artery and is recognized as the main cause of cardiovascular disease and the leading cause of death worldwide.•Optical Coherence Tomography (OCT) is a turning point and powerful tool in cardiovascular imaging for coronary plaque indication. But it has limitations to be addressed.•Understanding the plaque morphology and its sub-components is primordial to assist clinicians by providing them with the accurate information of plaque progression.•Based on discussions with different interventional cardiologists, it is realized that there is a clear need for an automatic fully analytical model based on machine learning and deep learning techniques to accurately evaluate coronary artery in real-time using OCT imaging.•By reviewing the existing technical studies, we divided different steps of the coronary analytical model into three categories of segmentation, classification, and prediction not only to indicate coronary plaque morphology, but also to predict the plaques prone to rupture using various machine learning approaches. 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.