This article sets out to determine whether machine learning can be used to train and calibrate the signature for diagnosing hepatocellular carcinoma in...
Due to the high radiosensitivity of the fetus and embryo, diagnostic imaging procedures for pregnant patients raise health concerns. Therefore, the authors of...
In an attempt to determine whether deep learning with the convolutional neural networks (CNN) can be used for identifying parkinsonian disorder on MRI, the...
The goal of this study was to test if using AVERT™ (a 33-point semi-automated program developed for VF diagnosis in adults) is better for morphometric...
The authors of this study recognized the potential of multiparametric positron emission tomography/magnetic resonance imaging (mpPET/MRI) for detecting and...
The goal of this study was to develop and authenticate an MRI-based radiomics strategy for the preoperative estimation of pathological grade in bladder cancer...
This study aimed to assess whether MRI radiomics can categorize ovarian masses and to determine the association between MRI radiomics and survival among...
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...
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.