In the ever-evolving landscape of radiology, the quest for enhanced image quality and reduced noise, particularly in obese patients, remains an enduring...
Over a decade in the making, the novel concept of radiomics has been silently brewing, promising to reshape the landscape of personalised and precision...
This study evaluates deep learning (DL) algorithms that are playing an increasingly important role in automatic medical image analysis. The DL algorithm used...
A new study sees the development of a fully automatic framework for the diagnosis of the cause of left ventricular hypertrophy (LVH) via cardiac cine images....
The aim of this study was to evaluate the performance of the LungQuant system, which is a deep learning-based software for quantitative analysis of chest CT....
The aim of this study was to present an in vivo stability analysis of radiomic features for pulmonary nodules against varying radiation dose levels. The...
The authors of this study evaluated the reproducibility of a deep learning and optimal-surface graph-cut method to automatically segment the airway lumen and...
This study conducted a bibliometric analysis of radiomics ten years after the first work became available in March 2012. Throughout the analysis, the authors...
The authors of this study developed a CT-based artificial intelligence model with the ability to differentiate between benign and malignant ovarian tumors,...
Deep learning methods to quantitatively assess disease-specific brain atrophy from CT and MRI images are rapidly gaining popularity, and a new era of clinical...
Although structured reporting (SR) is recommended in the field of radiology compared to free-text reporting (FTR), the use of SR still experiences obstacles...
The purpose of this study was to establish a robust interpretable deep learning (DL) model for the automatic noninvasive grading of meningiomas along with...
This study seeks to determine if artificial intelligence (AI)-based software can improve radiologists’ performance when detecting clinically significant...
The authors of this study proposed a multi-task U-Net-based architecture to jointly estimate water-only and fat-only images. This approach allowed for the...
Our recent research published in European Radiology aimed to evaluate the impact of hepatic steatosis (HS) on liver volume by conducting a retrospective...
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.