Deep learning reconstruction (DLR) is a novel method of reconstruction that introduces deep convolutional neural networks into the reconstruction flow. The...
Convolutional neural networks (CNN) have demonstrated the potential to become effective and accurate decision support tools for radiologists. A major barrier...
The aim of this study was to determine the effects of different reconstruction algorithms on histogram and texture features in different targets. The authors...
The main goal of this retrospective two-center study was to develop a radiomics model with all-relevant imaging features from multiphasic computed tomography...
This pilot study aims to investigate whether liver fibrosis can be staged by deep learning techniques based on CT images. It included CT examinations of...
This article attempts to predict the rupture risk in anterior communicating artery (ACOM) aneurysms by using a two-layer feed-forward artificial neural...
The aim of this survey was to assess undergraduate medical students’ attitudes towards artificial intelligence (AI) in radiology and medicine. A web-based...
This article provides an overview of the history of AI methods for radiological image analysis with the aim to deliver a context for the latest developments....
The topic of artificial intelligence (AI) has become one of the main points of discussion in the field of radiology and medicine. Through discussions on...
Welcome to the blog on Artificial Intelligence of the European Society of Radiology
This blog aims at bringing educational and critical perspectives on AI to readers. It should help imaging professionals to learn and keep up to date with the technologies being developed in this rapidly evolving field.
