machine learning

Practical applications of deep learning: classifying the most common categories of plain radiographs in a PACS using a neural network

The purpose of this study was to classify the most common types of plain radiography through the use of a neural network and, subsequently, to validate the...

European Radiology

2 weeks ago

Improved long-term prognostic value of coronary CT angiography-derived plaque measures and clinical parameters on adverse cardiac outcome using machine learning

In recent years, artificial intelligence (AI) and, in particular, the application of machine learning (ML) algorithms, have become a new cornerstone in...

Christian Tesche

4 months ago

Preoperative prediction for pathological grade of hepatocellular carcinoma via machine learning–based radiomics

The purpose of this single-center retrospective study was to investigate the effectiveness of contrast-enhanced computed tomography (CECT)-based radiomic...

European Radiology

4 months ago

Improved characterization of sub-centimeter enhancing breast masses on MRI with radiomics and machine learning in BRCA mutation carriers

This retrospective study aimed to investigate whether radiomics features extracted from MRI of BRCA-positive patients with sub-centimeter breast masses can be...

European Radiology

5 months ago

CT-based radiomics and machine learning to predict spread through air space in lung adenocarcinoma

The authors of this retrospective study aimed to develop and validate a CT-based radiomics model for preoperative prediction of spread through air space...

European Radiology

6 months ago

Machine learning and radiomic phenotyping of lower grade gliomas: improving survival prediction

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...

European Radiology

7 months ago

AI abstracts from ECR 2019: analysis of topics and compliance with the STARD for abstracts checklist

New machine learning techniques, especially deep neural networks, hold the promise of revolutionizing many aspects of radiology and have gained immense public...

Daniel Pinto dos Santos

8 months ago

Diagnostic accuracy and potential covariates for machine learning to identify IDH mutations in glioma patients: evidence from a meta-analysis

The goal of this study was to assess the diagnostic accuracy of machine learning in the prediction of isocitrate dehydrogenase (IDH) mutations, particularly...

European Radiology

8 months ago

Can training data help radiologists to open deep learning black box?

Deep learning has recently pervaded the radiology field, reaching promising results that have encouraged both scientists and entrepreneurs to apply these...

Marina Codari

9 months ago

Development and validation of machine learning prediction model based on computed tomography angiography–derived hemodynamics for rupture status of intracranial aneurysms: a Chinese multicenter study

Decisions regarding the optimal management of unruptured intracranial aneurysms (UIAs) depend on a comprehensive comparison of the risks between aneurysm...

Guo Zhong Chen

10 months ago