Artificial intelligence in computed tomography plaque characterization: A review

Abstract

Cardiovascular disease (CVD) is associated with high mortality around the world. Prevention and early diagnosis are key targets in reducing the socio-economic burden of CVD.Artificial intelligence (AI) has experienced a steady growth due to technological innovations that have to lead to constant development. Several AI algorithms have been applied to various aspects of CVD in order to improve the quality of image acquisition and reconstruction and, at the same time adding information derived from the images to create strong predictive models. In computed tomography angiography (CTA), AI can offer solutions for several parts of plaque analysis, including an automatic assessment of the degree of stenosis and characterization of plaque morphology. A growing body of evidence demonstrates a correlation between some type of plaques, so-called high-risk plaque or vulnerable plaque, and cardiovascular events, independent of the degree of stenosis. The radiologist must apprehend and participate actively in developing and implementing AI in current clinical practice.In this current overview on the existing AI literature, we describe the strengths, limitations, recent applications, and promising developments of employing AI to plaque characterization with CT.

Long-COVID diagnosis: From diagnostic to advanced AI-driven models

“…In the era of modern medicine, artificial intelligence (AI) is a growing field of interest in diagnostic imaging due to technological innovations that have led to constant development [45] , [46] .…”

“…Similar findings have also been described for coronary artery disease [65] , given the relationship between carotid and coronary atherosclerosis [66] . The application of AI algorithms in cardiovascular imaging is an evolving field that has shown to be an efficient tool for the diagnosis of atherosclerotic disease as well as for plaque characterization [45] . Various AI-trained models that combined imaging data and clinical risk factors have been applied to aid in predicting outcomes [67] , [68] , [69] , [70] , [71] .…”

Artificial Intelligence in Coronary CT Angiography: Current Status and Future Prospects

“…In the face of a large number of CT images, missed diagnosis and misdiagnosis might be caused by visual fatigue. To detect patients who may suffer from CHD from the CCTA images, it is necessary to visually evaluate the plaque and measure the stenosis; this is a tedious and timeconsuming process (52). AI can quantify the underlying concepts of textures and structures, input certain characteristics into the machine learning model, and automatically complete the plaque analysis and stenosis rate assessment, therefore greatly reducing the actual burden of imaging workers (Figure 3) (49).…”

Algoritmos Artificiais Superam os Modelos Tradicionais na Predição de Doença Arterial Coronariana

“…Discutimos os méritos, limites, novas aplicações e possíveis avanços do uso de IA para caracterizar placas usando TC nesta revisão de literatura. 12 …”

“…We discuss the merits, limits, new applications, and potential advancements of using AI to characterize plaques using CT in this current literature review. 12 …”