The risk of coronary heart disease (CHD) clinical manifestations and patient management is
estimated according to risk scores accounting multifactorial risk factors, thus failing to cover …

The technological development of Artificial Intelligence (AI) has grown rapidly in recent
years. The applications of AI to cardiovascular imaging are various and could improve the …

Background Studies have rarely investigated stenosis detection from head and neck CT
angiography scans because accurate interpretation is time consuming and labor intensive …

Objectives To investigate the effect of reader experience, calcification and image quality on
the performance of deep learning (DL) powered coronary CT angiography (CCTA) in …

Background: The presence of heavy calcification in the coronary artery always presents a
challenge for coronary computed tomography angiography (CCTA) in assessing the degree …

Background Transcatheter arterial chemoembolization (TACE) is the mainstay of therapy for
intermediate-stage hepatocellular carcinoma (HCC); yet its efficacy varies between patients …

The purpose of this study was to finetune a deep learning model, real-enhanced super-
resolution generative adversarial network (Real-ESRGAN), and investigate its diagnostic …

Coronary artery disease is a major cause of morbidity and mortality worldwide. Its underlying
histopathology is the atherosclerotic plaque, which comprises lipid, fibrous and—when …

Abstract SARS-CoV-2 infection, responsible for COVID-19 outbreak, can cause cardiac
complications, worsening outcome and prognosis. In particular, it can exacerbate any …

Purpose The purpose of this study was to evaluate the capabilities of photon-counting (PC)
CT combined with artificial intelligence-derived coronary computed tomography …