Rationale: Deep learning is a powerful tool that may allow for improved outcome prediction.
Objectives: To determine if deep learning, specifically convolutional neural network (CNN) …

Heavy smokers undergoing screening with low-dose chest CT are affected by
cardiovascular disease as much as by lung cancer. Low-dose chest CT scans acquired in …

Abstract Purpose of Review To summarize current artificial intelligence (AI)-based
applications for coronary artery calcium scoring (CACS) and their potential clinical impact …

Cardiovascular disease (CVD) is the global leading cause of death. A strong risk factor for
CVD events is the amount of coronary artery calcium (CAC). To meet the demands of the …

Objective: To generate a convolutional neural network that inputs a non-contrast chest CT
scan and outputs the Agatston score associated with it directly, without a prior segmentation …

Background Compared to classical computer-aided analysis, deep learning and in particular
deep convolutional neural network demonstrates breakthrough performance in many of the …

Objectives To develop a fully automatic multiview shape constraint framework for
comprehensive coronary artery calcium scores (CACS) quantification via deep learning on …

Low-Dose CT (LDCT) can significantly improve the accuracy of lung cancer diagnosis and
thus reduce cancer deaths compared to chest X-ray. The lung cancer risk population is also …

Materials and methods: We use a large database of 9,925 CT scans to train, validate and
test the network for which reference standard BMD and percentage emphysema have been …

Purpose We aimed to develop a deep learning (DL)-based algorithm for automated
quantification of aortic valve calcium (AVC) from non-enhanced electrocardiogram-gated …