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Back to main AI Blog

machine learning

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.

Most used hashtags:

machine learningdeep learningradiomicsmrix-ray computed tomographyai newsCOVID-19mammographyinterviewbreast cancerdiagnostic imagingneural networkstomographyworkflowMDCTradiography

Latest posts

Clinical applications of AI and radiomics in neuro-oncology

Insights into Imaging May 2022

In this educational review, the authors take a comprehensive look at various aspects and applications of artificial intelligence (AI) in the field of neuro-oncology, including machine learning, deep learning, and radiomics. The merits and challenges of the deployment and use of AI tools in neuro-oncology are put under the microscope, with the authors concluding that AI has a promising future

Read More →

Can machine learning predict the WHO/ISUP nuclear grade of clear cell renal cell carcinomas?

Insights into Imaging May 2022

In this retrospective study, the authors investigated if radiomics features extracted from nephrographic-phase (NP) CT images combined with clinicoradiological characteristics may have the potential to preoperatively differentiate between low- and high-nuclear grade of clear cell renal cell carcinomas (CCRCCs). They were able to demonstrate that radiomics analysis may be used as a potentially noninvasive method for distinguishing low- from high-grade

Read More →

Measuring bias when using cross-validation in radiomics

Aydin Demircioğlu February 2022

Radiomics studies often perform a feature selection step to remove redundant and irrelevant features from the generic features extracted from radiological images. However, one must take care if feature selection is used together with cross-validation. In this case, the feature selection must be applied to each fold separately. If it is applied beforehand on all data as a preprocessing step,

Read More →

Artificial neural network detects contrast phase in MDCT

Sebastian Rühling February 2022

Automated extraction of novel biomarkers from routine imaging examinations, e.g., opportunistic CT, is a promising opportunity to extend current screening possibilities and to better guide individualized therapies. In theory, opportunistic CT can be used to obtain quantitative biomarker data from any tissue included in a routine examination. Over the past decade, AI has been used to develop many different automated

Read More →

Evaluation of a CTA-based convolutional neural network for infarct volume prediction in anterior cerebral circulation ischaemic stroke

European Radiology Experimental December 2021

The authors of this study aimed to determine the efficacy of a convolutional neural network (CNN) in final infarct volume prediction from computed tomography angiography (CTA), subsequently comparing the results to a CT perfusion (CTP)-based commercially available software. The stroke cases treated with thrombolytic therapy or receiving supportive care were retrospectively selected by the authors. The study found that a

Read More →

Clinical value of radiomics and machine learning in breast ultrasound: a multicenter study for differential diagnosis of benign and malignant lesions

Arnaldo Stanzione September 2021

Beyond hopes and hype, the clinical applicability of artificial intelligence decision support tools deserves to be rigorously explored in multicenter settings. With this work, we aimed to do so by focusing our attention on solid breast cancer lesions as detected by ultrasound. Indeed, the journey of a patient usually begins with this first-line imaging modality and fast, non-invasive and cost-effective

Read More →

COVID-19 classification of X-ray images using deep neural networks

European Radiology September 2021

The authors of this retrospective study propose a deep learning model for the detection of COVID-19 from chest x-rays (CXRs), as well as a tool for retrieving similar patients according to the model’s results on their CXRs. The data used for training and evaluating this model was collected from inpatients across four different hospitals. The proposed model achieved accuracy of

Read More →

Machine learning based on the multimodal connectome can predict the preclinical stage of Alzheimer’s disease: a preliminary study

European Radiology September 2021

Subjective cognitive decline (SCD) may be a preclinical stage of Alzheimer’s disease (AD). For this preliminary study, the authors recruited one hundred ten SCD individuals and well-matched healthy controls (HCs) in order to find if machine learning based on the multimodal connectome could predict the preclinical stage of AD. The study found that the characteristics identified from the multimodal network

Read More →

How to read and review papers on machine learning and artificial intelligence in radiology: a survival guide to key methodological concepts

European Radiology May 2021

In this article, the authors aimed to guide and inform the radiology community regarding key methodological aspects of machine learning (ML) in order to improve their academic reading and peer-review experience. This was done so within four broad categories: study design, data handling, modelling, and reporting. Key points Machine learning is new and rather complex for the radiology community. Validity,

Read More →

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

European Radiology March 2021

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 network’s performance on internal and external data. The authors used data from a single institution when classifying the most common categories of radiographs. This study resulted in the authors determining that it is

Read More →

Clinical applications of AI and radiomics in neuro-oncology

Insights into Imaging May 2022

In this educational review, the authors take a comprehensive look at various aspects and applications of artificial intelligence (AI) in the field of neuro-oncology, including machine learning, deep learning, and radiomics. The merits and challenges of the deployment and use of AI tools in neuro-oncology are put under the microscope, with the authors concluding that AI has a promising future

Read More →

Can machine learning predict the WHO/ISUP nuclear grade of clear cell renal cell carcinomas?

Insights into Imaging May 2022

In this retrospective study, the authors investigated if radiomics features extracted from nephrographic-phase (NP) CT images combined with clinicoradiological characteristics may have the potential to preoperatively differentiate between low- and high-nuclear grade of clear cell renal cell carcinomas (CCRCCs). They were able to demonstrate that radiomics analysis may be used as a potentially noninvasive method for distinguishing low- from high-grade

Read More →

Measuring bias when using cross-validation in radiomics

Aydin Demircioğlu February 2022

Radiomics studies often perform a feature selection step to remove redundant and irrelevant features from the generic features extracted from radiological images. However, one must take care if feature selection is used together with cross-validation. In this case, the feature selection must be applied to each fold separately. If it is applied beforehand on all data as a preprocessing step,

Read More →

Artificial neural network detects contrast phase in MDCT

Sebastian Rühling, Fernando Navarro February 2022

Automated extraction of novel biomarkers from routine imaging examinations, e.g., opportunistic CT, is a promising opportunity to extend current screening possibilities and to better guide individualized therapies. In theory, opportunistic CT can be used to obtain quantitative biomarker data from any tissue included in a routine examination. Over the past decade, AI has been used to develop many different automated

Read More →

Evaluation of a CTA-based convolutional neural network for infarct volume prediction in anterior cerebral circulation ischaemic stroke

European Radiology Experimental December 2021

The authors of this study aimed to determine the efficacy of a convolutional neural network (CNN) in final infarct volume prediction from computed tomography angiography (CTA), subsequently comparing the results to a CT perfusion (CTP)-based commercially available software. The stroke cases treated with thrombolytic therapy or receiving supportive care were retrospectively selected by the authors. The study found that a

Read More →

Clinical value of radiomics and machine learning in breast ultrasound: a multicenter study for differential diagnosis of benign and malignant lesions

Arnaldo Stanzione September 2021

Beyond hopes and hype, the clinical applicability of artificial intelligence decision support tools deserves to be rigorously explored in multicenter settings. With this work, we aimed to do so by focusing our attention on solid breast cancer lesions as detected by ultrasound. Indeed, the journey of a patient usually begins with this first-line imaging modality and fast, non-invasive and cost-effective

Read More →

COVID-19 classification of X-ray images using deep neural networks

European Radiology September 2021

The authors of this retrospective study propose a deep learning model for the detection of COVID-19 from chest x-rays (CXRs), as well as a tool for retrieving similar patients according to the model’s results on their CXRs. The data used for training and evaluating this model was collected from inpatients across four different hospitals. The proposed model achieved accuracy of

Read More →

Machine learning based on the multimodal connectome can predict the preclinical stage of Alzheimer’s disease: a preliminary study

European Radiology September 2021

Subjective cognitive decline (SCD) may be a preclinical stage of Alzheimer’s disease (AD). For this preliminary study, the authors recruited one hundred ten SCD individuals and well-matched healthy controls (HCs) in order to find if machine learning based on the multimodal connectome could predict the preclinical stage of AD. The study found that the characteristics identified from the multimodal network

Read More →

How to read and review papers on machine learning and artificial intelligence in radiology: a survival guide to key methodological concepts

European Radiology May 2021

In this article, the authors aimed to guide and inform the radiology community regarding key methodological aspects of machine learning (ML) in order to improve their academic reading and peer-review experience. This was done so within four broad categories: study design, data handling, modelling, and reporting. Key points Machine learning is new and rather complex for the radiology community. Validity,

Read More →

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

European Radiology March 2021

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 network’s performance on internal and external data. The authors used data from a single institution when classifying the most common categories of radiographs. This study resulted in the authors determining that it is

Read More →

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  • Reduced registration fees for ECR 1
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  • Content e-mails for all 3 ESR journals 4
  • Updates on offers & events through our newsletters
  • Exclusive access to the ESR feed in Juisci

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Footnotes:

01

Reduced registration fees for ECR 2024:
Provided that ESR 2023 membership is activated and approved by August 31, 2023.

Reduced registration fees for ECR 2025:
Provided that ESR 2024 membership is activated and approved by August 31, 2024.

02
Not all activities included
03
Examination based on the ESR European Training Curriculum (radiologists or radiology residents).
04
European Radiology, Insights into Imaging, European Radiology Experimental.