Recent Trends in Artificial Intelligence-Assisted Coronary Atherosclerotic Plaque Characterization

Anjan Gudigar, Sneha Nayak, Jyothi Samanth, U Raghavendra, Ashwal A J, Prabal Datta Barua, Md Nazmul Hasan, Edward J. Ciaccio, Ru-San Tan and U. Rajendra Acharya
Additional contact information
Anjan Gudigar: Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India
Sneha Nayak: Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India
Jyothi Samanth: Department of Cardiovascular Technology, Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal 576104, India
U Raghavendra: Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India
Ashwal A J: Department of Cardiology, Kasturba Medical College, Manipal Academy of Higher Education, Manipal 576104, India
Prabal Datta Barua: School of Management & Enterprise, University of Southern Queensland, Toowoomba, QLD 4350, Australia
Md Nazmul Hasan: Department of Cardiology, Ad-Din Medical College Hospital, Dhaka 1217, Bangladesh
Edward J. Ciaccio: Department of Medicine, Division of Cardiology, Columbia University Medical Center, New York, NY 10032, USA
Ru-San Tan: Department of Cardiology, National Heart Centre Singapore, Singapore 169609, Singapore
U. Rajendra Acharya: School of Engineering, Ngee Ann Polytechnic, Clementi 599489, Singapore

IJERPH, 2021, vol. 18, issue 19, 1-27

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.

Keywords: artificial intelligence; computer aided diagnosis; coronary angiography; coronary artery disease; coronary computed tomographic angiography; intravascular optical coherence tomography; intravascular ultrasound (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1660-4601/18/19/10003/pdf (application/pdf)
https://www.mdpi.com/1660-4601/18/19/10003/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:18:y:2021:i:19:p:10003-:d:641307

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().