Aims Traditional prognostic risk assessment in patients undergoing non-invasive imaging is
based upon a limited selection of clinical and imaging findings. Machine learning (ML) can …

Introduction Machine learning (ML) is a field in computer science that demonstrated to
effectively integrate clinical and imaging data for the creation of prognostic scores. The …

Objectives The aim of this study was to evaluate whether machine learning (ML) of
noncontrast computed tomographic (CT) and clinical variables improves the prediction of …

Background Machine learning algorithms hold potential for improved prediction of all-cause
mortality in cardiovascular patients, yet have not previously been developed with high …

Objectives The goal of this study was to investigate the association of stenosis and plaque
features with myocardial ischemia and their prognostic implications. Background Various …

Background Binary diagnosis of coronary artery disease does not preserve the complexity of
disease or quantify its severity or its associated risk with death; hence, a quantitative marker …

Background Coronary CT angiography contains prognostic information but the best method
to extract these data remains unknown. Purpose To use machine learning to develop a …

Objectives: This study was designed to assess the prognostic value of a new comprehensive
coronary computed tomography angiography (CTA) score compared with the stenosis …

Background and aims We sought to assess the performance of a comprehensive machine
learning (ML) risk score integrating circulating biomarkers and computed tomography (CT) …

Invasive coronary angiography imposes risks and high medical costs. Therefore, accurate,
reliable, non-invasive, and cost-effective methods for diagnosing coronary stenosis are …