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ultrasonography

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:

deep learningmachine learningmriradiomicsx-ray computed tomographyinterviewai newsmammographyCOVID-19diagnostic imagingneural networksbreast cancerbreast neoplasmsworkflowtomographyradiography

Latest posts

Using commercial AI-based mammography analysis software to improve breast US interpretations

European Radiology January 2025

This study evaluated the use of commercial AI-based mammography software to improve breast ultrasound (US) lesion interpretations. The AI software, which provides an AI malignancy score ranging from 0 to 100, was tested on 1,109 breasts that underwent both mammography and US-guided breast biopsy. The AI software showed an area under the curve (AUROC) of 0.79 for distinguishing benign from

Read More →

Deep Learning-based framework shown as a valuable tool to determine the composition of thyroid nodules

European Radiology May 2024

The authors of this retrospective multicenter study proposed a deep learning-based framework to identify the composition of thyroid nodules while also assessing their malignancy risk. Their research demonstrated that convolutional neural networks (CNNs) were able to assist in the diagnosis of thyroid nodules and reduce the rate of unnecessary fine-needle aspiration. Key points: Article: Deep learning to assist composition classification

Read More →

Deep learning radiomics in prediction of NAC response

European Radiology April 2022

For breast cancer, the standard of treatment for most patients is neoadjuvant chemotherapy (NAC), but response rates may vary among patients, causing delays in appropriate treatment. The authors of this prospective study aimed to investigate the feasibility of deep learning radiomics (DLR) in the prediction of NAC response at an early stage of breast cancer treatment. The authors found that

Read More →

Combining radiomics with ultrasound-based risk stratification systems for thyroid nodules: an approach for improving performance

European Radiology April 2021

In this study, the authors develop a radiomics score using ultrasound images in order to predict thyroid malignancy and to investigate its potential as a complementary tool to improve the performance of risk stratification systems. They found that radiomics was able to help predict malignancy in thyroid nodules in combination with risk stratification systems by improving specificity, accuracy, positive predictive

Read More →

Accurate prediction of responses to transarterial chemoembolization for patients with hepatocellular carcinoma by using artificial intelligence in contrast-enhanced ultrasound

European Radiology April 2020

The aim of this study was to establish and validate an artificial intelligence-based radiomics strategy in order to predict personalized responses to hepatocellular carcinoma (HCC) to first transarterial chemoembolization (TACE) by analyzing contrast-enhanced ultrasound (CEUS) cines quantitatively. This was done using 130 HCC patients, showing that a deep learning-based radiomics method can effectively utilize CEUS, resulting in accurate and personalized

Read More →

A pilot study on the performance of machine learning appled to texture-analysis-derived features for breast lesion characterisation at ABUS

European Radiology Experimental December 2019

In this study, the authors aimed to determine whether features derived from texture analysis (TA) are able to distinguish between normal, benign, and malignant tissue on automated breast ultrasound (ABUS). This pilot study included 54 women who underwent ABUS with benign and malignant solid breast lesions. The authors concluded that TA, in combination with machine learning (ML), may very well

Read More →

Automated multiparametric localization of prostate cancer based on B-mode, shear-wave elastography, and contrast-enhanced ultrasound radiomics

European Radiology November 2019

In this study, the authors aimed to assess the potential of machine learning (ML) based on B-mode, shear-wave elastography (SWE), and dynamic contrast-enhanced ultrasound (DCE-US) radiomics for the localization of prostate cancer lesions using transrectal ultrasound. The authors were able to demonstrate the technical feasibility of multiparametric ML to improve upon single US modalities for the localization of prostate cancer.

Read More →

Using commercial AI-based mammography analysis software to improve breast US interpretations

European Radiology January 2025

This study evaluated the use of commercial AI-based mammography software to improve breast ultrasound (US) lesion interpretations. The AI software, which provides an AI malignancy score ranging from 0 to 100, was tested on 1,109 breasts that underwent both mammography and US-guided breast biopsy. The AI software showed an area under the curve (AUROC) of 0.79 for distinguishing benign from

Read More →

Deep Learning-based framework shown as a valuable tool to determine the composition of thyroid nodules

European Radiology May 2024

The authors of this retrospective multicenter study proposed a deep learning-based framework to identify the composition of thyroid nodules while also assessing their malignancy risk. Their research demonstrated that convolutional neural networks (CNNs) were able to assist in the diagnosis of thyroid nodules and reduce the rate of unnecessary fine-needle aspiration. Key points: Article: Deep learning to assist composition classification

Read More →

Deep learning radiomics in prediction of NAC response

European Radiology April 2022

For breast cancer, the standard of treatment for most patients is neoadjuvant chemotherapy (NAC), but response rates may vary among patients, causing delays in appropriate treatment. The authors of this prospective study aimed to investigate the feasibility of deep learning radiomics (DLR) in the prediction of NAC response at an early stage of breast cancer treatment. The authors found that

Read More →

Combining radiomics with ultrasound-based risk stratification systems for thyroid nodules: an approach for improving performance

European Radiology April 2021

In this study, the authors develop a radiomics score using ultrasound images in order to predict thyroid malignancy and to investigate its potential as a complementary tool to improve the performance of risk stratification systems. They found that radiomics was able to help predict malignancy in thyroid nodules in combination with risk stratification systems by improving specificity, accuracy, positive predictive

Read More →

Accurate prediction of responses to transarterial chemoembolization for patients with hepatocellular carcinoma by using artificial intelligence in contrast-enhanced ultrasound

European Radiology April 2020

The aim of this study was to establish and validate an artificial intelligence-based radiomics strategy in order to predict personalized responses to hepatocellular carcinoma (HCC) to first transarterial chemoembolization (TACE) by analyzing contrast-enhanced ultrasound (CEUS) cines quantitatively. This was done using 130 HCC patients, showing that a deep learning-based radiomics method can effectively utilize CEUS, resulting in accurate and personalized

Read More →

A pilot study on the performance of machine learning appled to texture-analysis-derived features for breast lesion characterisation at ABUS

European Radiology Experimental December 2019

In this study, the authors aimed to determine whether features derived from texture analysis (TA) are able to distinguish between normal, benign, and malignant tissue on automated breast ultrasound (ABUS). This pilot study included 54 women who underwent ABUS with benign and malignant solid breast lesions. The authors concluded that TA, in combination with machine learning (ML), may very well

Read More →

Automated multiparametric localization of prostate cancer based on B-mode, shear-wave elastography, and contrast-enhanced ultrasound radiomics

European Radiology November 2019

In this study, the authors aimed to assess the potential of machine learning (ML) based on B-mode, shear-wave elastography (SWE), and dynamic contrast-enhanced ultrasound (DCE-US) radiomics for the localization of prostate cancer lesions using transrectal ultrasound. The authors were able to demonstrate the technical feasibility of multiparametric ML to improve upon single US modalities for the localization of prostate cancer.

Read More →

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①

Membership

for radiologists, radiology residents, professionals of allied sciences (including radiographers/radiological technologists, nuclear medicine physicians, medical physicists, and data scientists) & professionals of allied sciences in training residing within the boundaries of Europe

  • Reduced registration fees for ECR 1
  • Reduced fees for the European School of Radiology (ESOR) 2
  • Option to participate in the European Diploma. 3
  • Free electronic access to the journal European Radiology 
  • Content e-mails for all ESR journals4
  • Updates on offers & events through our newsletters
  • Exclusive access to the ESR feed in Juisci

€ 11 /year

Yes! That is less than €1 per month.
②

Free membership

for radiologists, radiology residents or professionals of allied sciences engaged in practice, teaching or research residing outside Europe as well as individual qualified professionals with an interest in radiology and medical imaging who do not fulfil individual or all requirements for any other ESR membership category & former full members who have retired from all clinical practice
  • Reduced registration fees for ECR 1
  • Option to participate in the European Diploma. 3
  • Free electronic access to the journal European Radiology
  • Content e-mails for all ESR journals 4
  • Updates on offers & events through our newsletters
  • Exclusive access to the ESR feed in Juisci

€ 0

The best things in life are free.
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  • Content e-mails for all 3 ESR journals 4
  • Updates on offers & events through our newsletters

€ 0

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Footnotes:

01

Reduced registration fees for ECR 2026:
Provided that ESR 2025 membership is activated and approved by August 31, 2025.

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.