Recent Trends in Artificial Intelligence-Assisted Coronary Atherosclerotic Plaque Characterization

被引：21

作者：

Gudigar, Anjan ^{[1
]}

Nayak, Sneha ^{[1
]}

Samanth, Jyothi ^{[2
]}

Raghavendra, U. ^{[1
]}

Ashwal, A. J. ^{[3
]}

Barua, Prabal Datta ^{[4
,5
]}

Hasan, Md Nazmul ^{[6
]}

Ciaccio, Edward J. ^{[7
]}

Tan, Ru-San ^{[8
,9
]}

Acharya, U. Rajendra ^{[10
,11
,12
]}

机构：

[1] Manipal Acad Higher Educ, Manipal Inst Technol, Dept Instrumentat & Control Engn, Manipal 576104, India

[2] Manipal Acad Higher Educ, Dept Cardiovasc Technol, Manipal Coll Hlth Profess, Manipal 576104, India

[3] Manipal Acad Higher Educ, Dept Cardiol, Kasturba Med Coll, Manipal 576104, India

[4] Univ Southern Queensland, Sch Management Enterprise, Toowoomba, Qld 4350, Australia

[5] Univ Technol Sydney, Fac Engn & Informat Technol, Sydney, NSW 2007, Australia

[6] Ad Din Med Coll Hosp, Dept Cardiol, Dhaka 1217, Bangladesh

[7] Columbia Univ, Div Cardiol, Dept Med, Med Ctr, New York, NY 10032 USA

[8] Natl Heart Ctr Singapore, Dept Cardiol, Singapore 169609, Singapore

[9] Duke NUS Med Sch, Singapore 169857, Singapore

[10] Ngee Ann Polytech, Sch Engn, Clementi 599489, Singapore

[11] Asia Univ, Dept Biomed Informat & Med Engn, Taichung 41354, Taiwan

[12] Singapore Univ Social Sci, Sch Sci & Technol, Dept Biomed Engn, Singapore S599494, Singapore

来源：

INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH | 2021年 / 18卷 / 19期

关键词：

artificial intelligence; computer aided diagnosis; coronary angiography; coronary artery disease; coronary computed tomographic angiography; intravascular optical coherence tomography; intravascular ultrasound; COMPUTED-TOMOGRAPHY ANGIOGRAPHY; INTRAVASCULAR ULTRASOUND IMAGES; FRACTIONAL FLOW RESERVE; TRANSLUMINAL ATTENUATION GRADIENT; ACUTE CHEST-PAIN; CT ANGIOGRAPHY; ARTERY STENOSIS; DIAGNOSTIC PERFORMANCE; NONINVASIVE ASSESSMENT; RISK STRATIFICATION;

D O I：

10.3390/ijerph181910003

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

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.

引用

页数：27