machine learning

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

Christian Tesche December 2020

In recent years, artificial intelligence (AI) and, in particular, the application of machine learning (ML) algorithms, have become a new cornerstone in cardiovascular imaging with improved decision pathways, risk stratification, and outcome prediction in a more objective, reproducible, and rational manner. The integration of ML in daily routine clinical practice may hold potential to improve imaging workflow and to promote

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

European Radiology December 2020

The purpose of this single-center retrospective study was to investigate the effectiveness of contrast-enhanced computed tomography (CECT)-based radiomic signatures for the preoperative prediction of pathological grades of hepatocellular carcinoma (HCC) via machine learning. The authors found that the radiomics signatures could non-invasively explore the underlying association between CECT images and pathological grades of HCC. Key points The radiomics signatures may

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

European Radiology November 2020

This retrospective study aimed to investigate whether radiomics features extracted from MRI of BRCA-positive patients with sub-centimeter breast masses can be coupled with machine learning in order to differentiate benign lesions from malignant lesions using model-free parameter maps. The authors determined that radiomics analysis coupled with machine learning does improve the diagnostic accuracy of MRI in characterizing sub-centimeter breast masses

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

European Radiology October 2020

The authors of this retrospective study aimed to develop and validate a CT-based radiomics model for preoperative prediction of spread through air space (STAS) in lung adenocarcinoma. They found that a CT-based radiomics model can preoperatively predict, with good diagnosis performance, STAS in lung adenocarcinoma. Key points CT-based radiomics and machine learning model can predict spread through air space (STAS)

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

European Radiology September 2020

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 gliomas over clinical isocitrate dehydrogenase (IDH) status. The authors extracted radiomic features from the preoperative MRI data of 296 lower grade glioma patients from their institution as well as The Cancer Genome Atlas (TCGA) and The

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

Daniel Pinto dos Santos August 2020

New machine learning techniques, especially deep neural networks, hold the promise of revolutionizing many aspects of radiology and have gained immense public and professional attention over the last few years. This has led to a sharp increase in publications, the founding of new journals, and FDA approval for new diagnostic algorithms. With this increased scientific output, we wanted to take

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

European Radiology August 2020

The goal of this study was to assess the diagnostic accuracy of machine learning in the prediction of isocitrate dehydrogenase (IDH) mutations, particularly in patients with glioma, as well as to identify potential covariates that may have an influence on the diagnostic performance of machine learning. The authors were able to show that machine learning demonstrated excellent diagnostic performance in

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

Marina Codari July 2020

Deep learning has recently pervaded the radiology field, reaching promising results that have encouraged both scientists and entrepreneurs to apply these models to improve patient care. However, “with great power there must also come — great responsibility” [1]! In most cases, the complexity of deep learning models forces their users, and sometimes also their developers, to treat them as black

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

Guo Zhong Chen June 2020

Decisions regarding the optimal management of unruptured intracranial aneurysms (UIAs) depend on a comprehensive comparison of the risks between aneurysm rupture and interventional treatment. The accurate prediction for UIA rupture risk is important for clinicians and patients. Our study further proves that the hemodynamic parameters can improve prediction performance for rupture status of UIAs. Moreover, the AUC of model integrating

Deep learning: definition and perspectives for thoracic imaging

European Radiology May 2020

The authors of this review aimed to provide definitions for understanding the methods of machine learning, deep learning, and convolutional neural networks (CNN) and to dive into their roles and potential in the area of thoracic imaging. Key points Deep learning outperforms other machine learning techniques for number of tasks in radiology. Convolutional neural network is the most popular deep

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

Christian Tesche December 2020

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

European Radiology December 2020

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

European Radiology November 2020

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

European Radiology August 2020

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

Marina Codari July 2020

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

Guo Zhong Chen June 2020

Deep learning: definition and perspectives for thoracic imaging

European Radiology May 2020

Welcome to the blog on Artificial Intelligence of
the European Society of Radiology

Latest posts

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

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

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

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

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

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

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

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

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

Deep learning: definition and perspectives for thoracic imaging

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

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

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

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

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

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

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

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

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

Deep learning: definition and perspectives for thoracic imaging

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machine learning

Welcome to the blog on Artificial Intelligence ofthe European Society of Radiology

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Footnotes:

Welcome to the blog on Artificial Intelligence of
the European Society of Radiology