The authors of this study aimed to determine the diagnostic performance of a deep learning algorithm for the automated detection of small 18F-FDG-avid...
The authors of this retrospective study performed test-retest reproducibility analyses for a deep learning-based automatic detection algorithm (DLAD) using...
The aim of this study was to establish and validate an artificial intelligence-based radiomics strategy in order to predict personalized responses to...
In this study, the authors aimed to build a dual-energy CT (DECT)-based deep learning radiomics nomogram that could be used for lymph node metastasis...
This study identified 236 patients from two cohorts who underwent surgery for ground-glass nodules (GGNs). The novel marginal features described, when...
In this study, the authors developed a deep feature fusion model (DFFM) in order to segment postoperative gliomas on CT images, which were guided by...
The authors of this study evaluated the impact of utilizing digital breast tomosynthesis (DBT) and/or full-field digital mammography (FFDM), and different...
In this study, the authors aimed to develop and validate a patient questionnaire on the patients’ view on the implementation of artificial intelligence...
The aim of this retrospective study was to develop non-invasive machine learning (ML) classifiers for predicting post-Glenn shunt patients with low and high...
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.