Recent Trends in Artificial Intelligence-Assisted Coronary Atherosclerotic Plaque Characterization.

Anjan Gudigar,Sneha Nayak,Prabal Datta Barua,U Raghavendra,Jyothi Samanth,Ashwal A J,Md Nazmul Hasan,Edward J Ciaccio,Ru-San Tan,U Rajendra Acharya

doi:10.3390/ijerph181910003

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.

Highlights

Coronary artery disease is a major cause of morbidity and mortality worldwide
Given the importance of early diagnosis and risk stratification, accurate, and efficient automated diagnostic tools for coronary plaque characterization based on non-invasive imaging modalities like coronary computed tomographic angiography (CCTA) are desirable
We present a systematic catalogue of computer aided diagnosis (CAD) using machine learning (ML) and deep learning (DL) techniques for coronary atherosclerotic plaque characterization using various imaging modalities

Summary

Introduction

Coronary artery disease is a major cause of morbidity and mortality worldwide. Its underlying histopathology is the formation of atherosclerotic plaques within the wall lining of the coronary artery tree [1]. Chronic morphological adaptation of the plaque is characterized by progressive necrotic changes and calcification [2]. Accumulation of plaque components increases the plaque atheroma volume, which encroaches on the coronary lumen [3,4]. Progressive arterial wall remodeling alters plaque composition and surface, rendering it vulnerable to erosion and even rupture [6]. This incites chain chemical reactions that precipitate acute thrombosis, which occludes the coronary lumen causing myocardial infarct [7]

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