Recent advancements in signal processing (SP) and machine learning, coupled with
electronic medical record keeping in hospitals and the availability of extensive sets of …

Cardiac imaging plays an important role in the diagnosis of cardiovascular disease (CVD).
Until now, its role has been limited to visual and quantitative assessment of cardiac structure …

Background Coronary inflammation induces dynamic changes in the balance between
water and lipid content in perivascular adipose tissue (PVAT), as captured by perivascular …

Radiomics is a rapidly developing field of research focused on the extraction of quantitative
features from medical images, thus converting these digital images into minable, high …

Objectives This study sought to determine whether coronary computed tomography
angiography (CCTA)-based radiomic analysis of pericoronary adipose tissue (PCAT) could …

Artificial intelligence (AI) is likely to revolutionize the way medical images are analysed and
has the potential to improve the identification and analysis of vulnerable or high-risk …

Cardiac computed tomography (CT) was initially developed as a non-invasive diagnostic
tool to detect and quantify coronary stenosis. Thanks to the rapid technological …

Chronic active lesions (CAL) are an important manifestation of chronic inflammation in
multiple sclerosis (MS) and have implications for non-relapsing biological progression. In …

Purpose The aim of this study was to develop radiomics–based machine learning models
based on extracted radiomic features and clinical information to predict the risk of death …

In daily clinical practice, clinicians integrate available data to ascertain the diagnostic and
prognostic probability of a disease or clinical outcome for their patients. For patients with …