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...
The object of this study was to assess the similarities and differences of radiomics features on full field digital mammography (FFDM) in FOR PROCESSING and...
The authors of this retrospective study had the goal of evaluating the effectiveness of radiomics signatures in order to predict the tumor response of...
In this educational review, the authors take a comprehensive look at various aspects and applications of artificial intelligence (AI) in the field of...
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.