Abstract Evaluation of coronary artery disease (CAD) using coronary computed tomography
angiography (CCTA) has seen a paradigm shift in the last decade. Evidence increasingly …

Artificial intelligence (AI) describes the use of computational techniques to perform tasks that
normally require human cognition. Machine learning and deep learning are subfields of AI …

Cardiovascular disease is difficult to detect due to several risk factors, including high blood
pressure, cholesterol, and an abnormal pulse rate. Accurate decision‐making and optimal …

Objective The decreased stability of atherosclerotic plaques increases the risk of ischemic
stroke. However, the specific characteristics of dysregulated immune cells and effective …

Background and aims The atherogenic index of plasma (AIP) has been suggested as a
marker of plasma atherogenicity. This study aimed to assess the association between AIP …

Cardiovascular disease (CVD) is the leading cause of mortality and disability in developed
countries. According to WHO, an estimated 17.9 million people died from CVDs in 2019 …

Percutaneous coronary intervention has been a standard treatment strategy for patients with
coronary artery disease with continuous ebullient progress in technology and techniques …

Atherosclerotic cardiovascular disease (ASCVD) is the most common cause of death
globally. Increasing amounts of highly diverse ASCVD data are becoming available and …

Background The difference between expert level (L3) reader and artificial intelligence (AI)
performance for quantifying coronary plaque and plaque components is unknown. Objective …

Background and aims The application of machine learning to assess plaque risk
phenotypes on cardiovascular CT angiography (CTA) is an area of active investigation …