In oncology, we are in the era of personalized medicine that enables increasingly precise, often molecular-based approaches (genomics, transcriptomics,...
The purpose of this retrospective study was to develop and evaluate the performance of U-Net to determine whether U-Net-based deep learning could accurately...
In this study, the authors aimed to build a dual-energy CT (DECT)-based deep learning radiomics nomogram that could be used for lymph node metastasis...
In this work, we aimed to evaluate the potential value of the machine learning (ML)–based MRI texture analysis for predicting 1p/19q codeletion status of...
The aim of this study was to develop a supervised machine learning (ML) algorithm that would use diffusion-weighted imaging-derived radiomic features to...
This study integrated the clinical data and radiomics signature generated by a support vector machine to establish a radiomics nomogram for prediction of...
The goal of this study was to measure the reproducibility of radiomic features in pancreatic parenchyma and ductal adenocarcinomas in patients who underwent...
This article sets out to determine whether machine learning can be used to train and calibrate the signature for diagnosing hepatocellular carcinoma in...
Despite the encouraging results, more studies are needed in order to further evaluate these preliminary findings and to find to what extent radiomics and AI...
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.
