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,...
As computed tomography (CT) sees an increase in utilization, inappropriate imaging has been seen as a significant concern; however, manual justification...
The authors of this study investigated whether radiomics based on T2-weighted MRI was able to discriminate between benign and borderline epithelial ovarian...
The authors of this narrative review aimed to introduce quality metrics for emerging artificial intelligence (AI) papers, such as the Checklist for Artificial...
The aim of this study was to provide an updated systematic review of radiomics in osteosarcoma, utilizing various databases such as PubMed, Embase, China...
In radiomics, the main goal is to extract quantitative features from medical data to train a predictive model using machine learning techniques. Contrary to...
This study, which included a cohort of 145 patients affected by lipomatous soft tissue tumours, aimed to compare the performances of MRI radiomic machine...
The authors of this study reviewed research on AI algorithms relating to computed tomography (CT) of the head in order to verify to what degree it is true...
The authors of this retrospective analysis looked at the role that radiomics played when applied to contrast-enhanced computed tomography (CT) in detecting...
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.