This retrospective study aimed to investigate whether radiomics features extracted from MRI of BRCA-positive patients with sub-centimeter breast masses can be...
This study used a sample of 131 participants who underwent low-dose computed tomography (LDCT) and standard-dose computed tomography (SDCT) to determine the...
The purpose of this study was to develop an automatic method for the identification and segmentation of clinically significant prostate cancer in low-risk...
The aim of this retrospective study was to establish and validate a radiomics nomogram that was based on contrast-enhanced spectral mammography (CESM) for the...
The authors of this retrospective study aimed to develop and validate a CT-based radiomics model for preoperative prediction of spread through air space...
In this study, the authors aimed to evaluate whether MRI-based radiomic features were able to improve the accuracy of survival predictions for lower grade...
In this observational cohort study, the authors aimed to determine the potential impact of machine learning (ML) CT-derived fractional flow reserve (CT-FFR)...
The goal of this study was to assess the diagnostic accuracy of machine learning in the prediction of isocitrate dehydrogenase (IDH) mutations, particularly...
The purpose of this study was to develop a deep learning-based method for the automated classification of renal cell carcinoma (RCC) from benign solid renal...
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.
