Abstract
At present, artificial intelligence (AI) has already been applied in cardiovascular imaging (e.g., image segmentation, automated measurements, and eventually, automated diagnosis) and it has been propelled to the forefront of cardiovascular medical imaging research. In this review, we presented the current status of artificial intelligence applied to image analysis of coronary atherosclerotic plaques, covering multiple areas from plaque component analysis (e.g., identification of plaque properties, identification of vulnerable plaque, detection of myocardial function, and risk prediction) to risk prediction. Additionally, we discuss the current evidence, strengths, limitations, and future directions for AI in cardiac imaging of atherosclerotic plaques, as well as lessons that can be learned from other areas. The continuous development of computer science and technology may further promote the development of this field.
Highlights
Modern medical care has increasingly advanced, cardiovascular disease (CVDs) that has an increasing incidence worldwide still poses a serious threat to the quality of human life and health
According to the latest report, CVDs remains the main cause of premature death in most countries, especially low- and middle-income countries [1], which suggests that treatment and prevention of CVDs still need to be improved [2]
Wilson et al [33] developed a of convolutional neural network (CNN) in identifying plaque properties in optical coherence tomography (OCT) images using line-based modeling methods, learning that CNN can significantly outperform in this task. They proposed a method based on the SegNet deep learning network, proving that the performance of the model was significantly improved compared with the previous method [34]
Summary
Modern medical care has increasingly advanced, cardiovascular disease (CVDs) that has an increasing incidence worldwide still poses a serious threat to the quality of human life and health. Coronary atherosclerosis underlies CAD and major adverse cardiac events (MACEs). Detection of these atherosclerotic plaques, identification of components, and assessment of their risk are essential for the management of patients with cardiovascular disease. Over the past two decades, various medical imaging techniques, including the invasive measurements such as optical coherence tomography (OCT), intravascular ultrasound (IVUS), and noninvasive measurements, such as computed tomography (CT), magnetic resonance imaging (MRI), and ultrasonography (US) have been developed for the assessment of coronary atherosclerosis [3]. Artificial intelligence (AI) is regarded as an exciting research topic in multifarious fields, as major advances in AI have occurred in recent years [6]. The application of artificial intelligence to the medical imaging field allows the identification of the information that improve clinical work efficiency. AI has recently been propelled to the forefront of cardiovascular medical imaging research [7,8]
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