In this study, the authors proposed a deep learning method for the detection and quantification of pneumothorax in heterogeneous routine clinical data, which...
The aim of this study was to evaluate whether machine learning algorithms allow for the prediction of Child-Pugh classification on clinical multiphase...
This preliminary study aimed to differentiate malignant from benign enhancing foci on breast MRI using radiomic signature. Forty-five patients were included...
In this study, the authors aimed to determine whether features derived from texture analysis (TA) are able to distinguish between normal, benign, and...
The authors of this study aimed to assess the performance of a convolutional neural network (CNN) algorithm to register cross-sectional liver imaging series...
The aim of this study was to develop a supervised machine learning (ML) algorithm that would use diffusion-weighted imaging-derived radiomic features to...
The authors of this study recognized the potential of multiparametric positron emission tomography/magnetic resonance imaging (mpPET/MRI) for detecting and...
In this study, the authors investigated how feasible it was to use 3D convolutional neural networks (CNN) to detect ischemic stroke from computed tomography...
This article sought to investigate the potential of generative models in the field of MRI of the spine, and did so by performing clinically relevant benchmark...
The aim of this narrative review is to take a broader look at the application of Artificial Intelligence (AI), primarily in medical imaging. The authors...
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.