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...
Magnetic Resonance Imaging (MRI) is a widely used medical imaging technology that is non-intrusive and considered safe for humans and can generate different...
The purpose of this study was to develop a deep-learning algorithm for tear detection in the anterior cruciate ligament (ACL), subsequently comparing its...
In this study, the authors’ aim was to investigate a previously developed radiomics-based biparametric magnetic resonance imaging (bpMRI) approach used...
The authors of this study conducted multiparametric magnetic resonance imaging (MRI)-derived radiomics based on multi-scale tumor region in order to predict...
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.