Recent Trends in Artificial Intelligence-Assisted Coronary Atherosclerotic Plaque Characterization

Gudigar, Anjan and Nayak, Sneha and Samanth, Jyothi and Raghavendra, U and Ashwal, AJ and Barua, Prabal Datta and Hasan, Md Nazmul and Ciaccio, Edward J and Tan, Ru-San and Acharya, Rajendra U (2021) Recent Trends in Artificial Intelligence-Assisted Coronary Atherosclerotic Plaque Characterization. International Journal of Environmental Research and Public Health. ISSN 1661-7827

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Official URL: https://www.mdpi.com/journal/ijerph

Abstract

Coronary artery disease is a major cause of morbidity and mortality worldwide. Its underlying histopathology is the atherosclerotic plaque, which comprises lipid, fibrous and—when chronic—calcium components. Intravascular ultrasound (IVUS) and intravascular optical coherence tomography (IVOCT) performed during invasive coronary angiography are reference standards for characterizing the atherosclerotic plaque. Fine image spatial resolution attainable with contemporary coronary computed tomographic angiography (CCTA) has enabled noninvasive plaque assessment, including identifying features associated with vulnerable plaques known to presage acute coronary events. Manual interpretation of IVUS, IVOCT and CCTA images demands scarce physician expertise and high time cost. This has motivated recent research into and development of artificial intelligence (AI)-assisted methods for image processing, feature extraction, plaque identification and characterization. We performed parallel searches of the medical and technical literature from 1995 to 2021 focusing respectively on human plaque characterization using various imaging modalities and the use of AI-assisted computer aided diagnosis (CAD) to detect and classify atherosclerotic plaques, including their composition and the presence of high-risk features denoting vulnerable plaques. A total of 122 publications were selected for evaluation and the analysis was summarized in terms of data sources, methods—machine versus deep learning—and performance metrics. Trends in AI-assisted plaque characterization are detailed and prospective research challenges discussed. Future directions for the development of accurate and efficient CAD systems to characterize plaque noninvasively using CCTA are proposed.

Item Type: Article
Uncontrolled Keywords: artificial intelligence; computer aided diagnosis; coronary angiography; coronary artery disease; coronary computed tomographic angiography; intravascular optical coherence tomography; intravascular ultrasound
Subjects: Medicine > KMC Manipal > Cardiology
Medicine > KMC Manipal > Cardiovascular & Thoracic Surgery
Engineering > MIT Manipal > Instrumentation and Control
Depositing User: MIT Library
Date Deposited: 11 Nov 2021 10:07
Last Modified: 11 Nov 2021 10:07
URI: http://eprints.manipal.edu/id/eprint/157700

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