The authors of this study aimed to classify motion-induced blurred images of calcified coronary plaques, in order to correct coronary calcium scored on...
This study integrated the clinical data and radiomics signature generated by a support vector machine to establish a radiomics nomogram for prediction of...
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...
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...
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.