The aim of this study was to investigate the effect of deep learning image reconstruction (DLIR) on the accuracy of iodine quantification and image quality of...
Automated extraction of novel biomarkers from routine imaging examinations, e.g., opportunistic CT, is a promising opportunity to extend current screening...
The authors of this study aimed to develop and validate a combined radiomics-clinical model to predict malignancy of cerebral compression fractures on CT....
In recent years, artificial intelligence (AI) and, in particular, the application of machine learning (ML) algorithms, have become a new cornerstone in...
The purpose of this single-center retrospective study was to investigate the effectiveness of contrast-enhanced computed tomography (CECT)-based radiomic...
The aim of this retrospective study was to develop non-invasive machine learning (ML) classifiers for predicting post-Glenn shunt patients with low and high...
In this study, using a semi-automatic software as a reference, the authors aimed to evaluate an artificial intelligence (AI)-based, automatic coronary artery...
The authors developed a radiomics model for predicting hematoma expansion in patients with intracerebral haemorrhage (ICH) and compared its predictive...
The authors of this study constructed two multivariate logistic regression models and compared the diagnostic performance between the two of them via receiver...
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.
