… We discuss a deep-learning-based approach for noninvasive computation of coronary
fractional flow reserve (FFR) from computed tomography images. The deep-learning model is …

… In this study, we determined the feasibility of analyzing computed tomography (CT) projection
data — sinograms — through a deep learning approach for human anatomy identification …

… During the latest years, artificial intelligence, and especially machine learning (ML), have …
deep learning, which utilizes multilayered neural networks. Cardiac computed tomography (CT…

… that machine learning algorithms could automatically analyze computed tomography (CT) of
… This demonstrates the positive impact of advanced machine learning in radiology workflow …

… Background: Chest computed tomography (CT) is crucial for the detection of lung cancer, …
Objective: The goal of the research was to generate reproducible machine learning modules …

… The aim of this study is to test whether sex prediction can be made by using machine
learning algorithms (ML) with parameters taken from computerized tomography (CT) images of …

The constantly increasing number of computed tomography (CT) examinations poses major
challenges for radiologists. In this article, the additional benefits and potential of an artificial …

Objectives Machine learning (ML) for medical imaging is emerging for several organs and
image modalities. Our objectives were to provide clinicians with an overview of this field by …

… machine learning-based method that evaluates for large vessel occlusion (LVO) and
ischemic core volume in patients using a widely available modality, computed tomography …

Background: Coronary computed tomographic angiography (CTA) is a reliable modality to
detect coronary artery disease. However, CTA generally overestimates stenosis severity …