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...
The authors of this study investigated whether radiomics based on T2-weighted MRI was able to discriminate between benign and borderline epithelial ovarian...
This study explores the importance of quality assurance when deploying an automatic segmentation model. The authors of this study built a deep-learning model...
In this study, the image quality and diagnostic performance of conventional motion-corrected periodically rotated overlapping parallel line with enhanced...
The authors of this narrative review aimed to introduce quality metrics for emerging artificial intelligence (AI) papers, such as the Checklist for Artificial...
This study, which included a cohort of 145 patients affected by lipomatous soft tissue tumours, aimed to compare the performances of MRI radiomic machine...
This comparative study aimed to evaluate the effectiveness of machine learning models based on morphological MRI radiomics in the classification of parotid...
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.