The authors of this study developed a CT-based artificial intelligence model with the ability to differentiate between benign and malignant ovarian tumors,...
This retrospective study investigated whether volumetric visceral adipose tissue (VAT) features that were extracted using radiomics and three-dimensional...
The authors of this study investigated whether radiomics based on T2-weighted MRI was able to discriminate between benign and borderline epithelial ovarian...
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...
When the Radiomics Quality Score (RQS) was presented to the scientific community back in 2017, its authors aimed to introduce a tool for a rapid and effective...
It is known that the reproducibility of radiomic features is influenced by myriad factors, one of which is the size of the segmented volume. We hypothesized...
The authors of this systematic review explored the currently available literature on artificial intelligence (AI) and radiomics applied to molecular imaging...
Due to the enormous amount of imaging data that is becoming available, there is a wide range of possible improvements that can be provided by artificial...
Breast cancer continues to be the most commonly diagnosed cancer among women with over 2 million new cases per year worldwide. One important independent risk...
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.
