Artificial Intelligence in Intracoronary Imaging

Abstract

Purpose of review: This paper investigates present uses and future potential of artificial intelligence (AI) applied to intracoronary imaging technologies.

Recent findings: Advances in data analytics and digitized medical imaging have enabled clinical application of AI to improve patient outcomes and reduce costs through better diagnosis and enhanced workflow. Applications of AI to IVUS and IVOCT have produced improvements in image segmentation, plaque analysis, and stent evaluation. Machine learning algorithms are able to predict future coronary events through the use of imaging results, clinical evaluations, laboratory tests, and demographics. The application of AI to intracoronary imaging holds significant promise for improved understanding and treatment of coronary heart disease. Even in these early stages, AI has demonstrated the ability to improve the prediction of cardiac events. Large curated data sets and databases are needed to speed the development of AI and enable testing and comparison among algorithms.

Keywords: Artificial intelligence; Deep learning; IVUS; Imaging; Machine learning; OCT.