This retrospective study investigated whether volumetric visceral adipose tissue (VAT) features that were extracted using radiomics and three-dimensional...
The purpose of this phantom study was the compare the image quality of a deep learning image reconstruction (DLIR) algorithm and conventional iterative...
This study aimed to develop a generative adversarial network (GAN) model to improve the image resolution of brain time-of-flight MR angiography (TOF-MRA), as...
In this study, the image quality and diagnostic performance of conventional motion-corrected periodically rotated overlapping parallel line with enhanced...
This retrospective study evaluated deep learning algorithms for the detection of automatic rib fracture on thoracic CT scans. The authors also aimed to...
The aim of this study was to investigate the effect of deep learning image reconstruction (DLIR) on the accuracy of iodine quantification and image quality of...
This comparative study aimed to evaluate the effectiveness of machine learning models based on morphological MRI radiomics in the classification of parotid...
The authors of this study used a deep learning-based approach, MOdality Mapping and Orchestration (MOMO), to deal with potential issues that are caused when...
Due to the challenges associated with differentiating COVID-19 from the number of respiratory infections that can appear on chest radiographs (CXR), the...
The authors of this study aimed to evaluate how artificial intelligence computer-aided detection (AI-CAD) differentiates lesions presenting as calcifications,...
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.