Development and application of artificial intelligence in cardiac imaging

Abstract

In this review, we describe the technical aspects of artificial intelligence (AI) in cardiac imaging, starting with radiomics, basic algorithms of deep learning and application tasks of algorithms, until recently the availability of the public database. Subsequently, we conducted a systematic literature search for recently published clinically relevant studies on AI in cardiac imaging. As a result, 24 and 14 studies using CT and MRI, respectively, were included and summarized. From these studies, it can be concluded that AI is widely applied in cardiac applications in the clinic, including coronary calcium scoring, coronary CT angiography, fractional flow reserve CT, plaque analysis, left ventricular myocardium analysis, diagnosis of myocardial infarction, prognosis of coronary artery disease, assessment of cardiac function, and diagnosis and prognosis of cardiomyopathy. These advancements show that AI has a promising prospect in cardiac imaging.

Figure 1.

Flowchart of literature review and selection. AI, artificial intelligence.

Figure 2.

The four major categories (with example applications) of artificial intelligence tasks in clinical cardiac radiology. CAC, coronary artery calcium; CAD, coronary artery disease; CCTA, coronary computedtomographic angiography; EAT, epicardial adipose tissue; FFR-CT, fractional flow reserve-CT; PVAT, perivascular adipose tissue.

Figure 3.

Schematic diagram of Shukun coronary artificial intelligence algorithm workflow. (A) axial image with annotations of aorta and coronary arteries (green); (B) VR image of initial segmentation including vessel image rupture (red) and coronary vein (blue); (C) VR image of corrected segmentation; (D) extracted centerline of coronary arteries; (E1) cMPR image with a detected stenosis (red); (E2) straightened rendering of the curved path shows a detected stenosis (red). cMPR, curved multiplanar reformation; VR, volume rendering.