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.

Artificial Intelligence Algorithm-Based Computerized Tomography Image Features Combined with Serum Tumor Markers for Diagnosis of Pancreatic Cancer

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Magnetic Resonance Texture Analysis in Myocardial Infarction

“…According to the data derived from AI, machine learning is a rapidly growing area that concentrates on building systems that make accurate predictions according to the data (28). Machine learning is mainly applied to establish the diagnosis of MI and assists differential diagnosis of acute mesenteric ischemia (AMI) and chronic mesenteric ischemia (CMI) that cannot be identified by the naked eyes (15, 29,30). The application of machine learning in medical imaging can be briefly summarized into three types: supervised, unsupervised, and semisupervised learning (31).…”

Imaging patients with stable chest pain special feature: introductory editorial

“…Furthermore, the collection gives a detailed review of the potential for functional coronary and cardiac CT imaging beyond the evaluation of the coronary artery lumen 22 , the potential role of noncardiac findings in risk stratification 23 and the role of machine learning to drive forward enhanced imaging data analysis. 24,25 In addition, the role of MRI for the assessment of chest pain is of great importance 26–28 , as is the role of imaging in the evaluation of heart valve disease 29 . Lastly, novel methods for the evaluation of heart viability and coronary artery disease 30 , with a focus on developing ways to assess vulnerable plaque 31,32 , are coming to the fore and are reviewed.…”