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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-19interviewmammographybreast cancerneural networksdiagnostic imagingtomographyMDCTradiographyworkflow

Latest posts

Prediction of lipomatous soft tissue malignancy on MRI

European Radiology Experimental February 2023

This study, which included a cohort of 145 patients affected by lipomatous soft tissue tumours, aimed to compare the performances of MRI radiomic machine learning analysis with that of deep learning in order to predict malignancy in patients with lipomas oratypical lipomatous tumours. The authors were able to show that batch-effect corrected machine learning and radiomics approaches outperformed deep learning-based

Read More →

Reproducibility of AI models in head CT

Insights into Imaging December 2022

The authors of this study reviewed research on AI algorithms relating to computed tomography (CT) of the head in order to verify to what degree it is true that AI software for applications in radiology must be transferable to other real-world problems. It was discovered that current research on AI for head CT is rarely reproducible, does not match with

Read More →

The role of radiomics in the detection of lymph node metastases

European Radiology Experimental December 2022

The authors of this retrospective analysis looked at the role that radiomics played when applied to contrast-enhanced computed tomography (CT) in detecting lymph node (LN) metastases in lung cancer patients compared to 18F-fluorodeoxyglucose positron emission tomography (PET)/CT as a reference. The authors determined that radiomics showed good discrimination power, regardless of the modelling technique, in detecting LN metastases in lung

Read More →

Machine learning–based radiomics classifies parotid tumors using morphological MRI

European Radiology December 2022

This comparative study aimed to evaluate the effectiveness of machine learning models based on morphological MRI radiomics in the classification of parotid tumors. The authors developed three-step machine learning models with extreme gradient boosting (XGBoost), support vector machine (SVM), and decision tree (DT) algorithms in order to classify the parotid neoplasms into four subtypes. The study was able to demonstrate

Read More →

Deep Learning–driven classification of external DICOM studies for PACS archiving

European Radiology November 2022

The authors of this study used a deep learning-based approach, MOdality Mapping and Orchestration (MOMO), to deal with potential issues that are caused when patients switch hospitals throughout the course of their treatment. These changes result in the staff at the new hospital, consisting of dedicated medical-technical personnel, being tasked with the processing and archiving of external DICOM studies. This

Read More →

AI for radiological paediatric fracture assessment

Susan Shelmerdine November 2022

Fractures in children are common, sometimes subtle and can signify underlying child abuse. They present unique challenges, given the different appearances of the growing skeleton at different ages. In this systematic review, the authors reviewed the available literature on the use of AI for paediatric fracture detection. Additional Key points: Few articles (n=9) were available for review regarding paediatric fracture

Read More →

MRI-based radiomics to predict response in locally advanced rectal cancer

European Radiology Experimental October 2022

The study aimed to implement and externally validate an MRI-based radiomics pipeline in order to predict the response to treatment of locally advanced rectal cancer (LARC), while also investigating the impact of manual and automatic segmentations on said radiomics models. The authors were able to show that radiomics models can help clinicians in the prediction of tumor response to chemoradiotherapy

Read More →

Applications of machine learning for imaging-driven diagnosis of musculoskeletal malignancies

European Radiology September 2022

Due to the rare nature of musculoskeletal malignancies and the lack of imaging data associated with this cancer, the authors of this study aimed to investigate whether machine learning (ML) is already having an impact on imaging-driven diagnosis of musculoskeletal malignancies. The authors concluded that because of the limited amounts of data and no established large-scale networks between multiple national

Read More →

Radiomic assessment of oesophageal adenocarcinoma

Insights into Imaging August 2022

The use of radiomic models offers a possible way to improve oesophageal adenocarcinoma assessment through quantitative image analysis, but model selection becomes complicated due to the myriad available predictors as well as the uncertainty of their relevance and reproducibility. Therefore, the aim of this study was to review recent research in order to facilitate precedent-based model selection for prospective validation

Read More →

Single-center versus multi-center biparametric MRI radiomics approach

Insights into Imaging June 2022

In this study, the authors’ aim was to investigate a previously developed radiomics-based biparametric magnetic resonance imaging (bpMRI) approach used for the discrimination of clinically significant peripheral zone prostate cancer (PZ csPCa) through the use of multi-center, multi-vendor (McMv) and single-center, single-vendor (ScSv) datasets. Using these datasets, the authors were able to determine that a single-center trained radiomics-based bpMRI model

Read More →

Prediction of lipomatous soft tissue malignancy on MRI

European Radiology Experimental February 2023

This study, which included a cohort of 145 patients affected by lipomatous soft tissue tumours, aimed to compare the performances of MRI radiomic machine learning analysis with that of deep learning in order to predict malignancy in patients with lipomas oratypical lipomatous tumours. The authors were able to show that batch-effect corrected machine learning and radiomics approaches outperformed deep learning-based

Read More →

Reproducibility of AI models in head CT

Insights into Imaging December 2022

The authors of this study reviewed research on AI algorithms relating to computed tomography (CT) of the head in order to verify to what degree it is true that AI software for applications in radiology must be transferable to other real-world problems. It was discovered that current research on AI for head CT is rarely reproducible, does not match with

Read More →

The role of radiomics in the detection of lymph node metastases

European Radiology Experimental December 2022

The authors of this retrospective analysis looked at the role that radiomics played when applied to contrast-enhanced computed tomography (CT) in detecting lymph node (LN) metastases in lung cancer patients compared to 18F-fluorodeoxyglucose positron emission tomography (PET)/CT as a reference. The authors determined that radiomics showed good discrimination power, regardless of the modelling technique, in detecting LN metastases in lung

Read More →

Machine learning–based radiomics classifies parotid tumors using morphological MRI

European Radiology December 2022

This comparative study aimed to evaluate the effectiveness of machine learning models based on morphological MRI radiomics in the classification of parotid tumors. The authors developed three-step machine learning models with extreme gradient boosting (XGBoost), support vector machine (SVM), and decision tree (DT) algorithms in order to classify the parotid neoplasms into four subtypes. The study was able to demonstrate

Read More →

Deep Learning–driven classification of external DICOM studies for PACS archiving

European Radiology November 2022

The authors of this study used a deep learning-based approach, MOdality Mapping and Orchestration (MOMO), to deal with potential issues that are caused when patients switch hospitals throughout the course of their treatment. These changes result in the staff at the new hospital, consisting of dedicated medical-technical personnel, being tasked with the processing and archiving of external DICOM studies. This

Read More →

AI for radiological paediatric fracture assessment

Susan Shelmerdine November 2022

Fractures in children are common, sometimes subtle and can signify underlying child abuse. They present unique challenges, given the different appearances of the growing skeleton at different ages. In this systematic review, the authors reviewed the available literature on the use of AI for paediatric fracture detection. Additional Key points: Few articles (n=9) were available for review regarding paediatric fracture

Read More →

MRI-based radiomics to predict response in locally advanced rectal cancer

European Radiology Experimental October 2022

The study aimed to implement and externally validate an MRI-based radiomics pipeline in order to predict the response to treatment of locally advanced rectal cancer (LARC), while also investigating the impact of manual and automatic segmentations on said radiomics models. The authors were able to show that radiomics models can help clinicians in the prediction of tumor response to chemoradiotherapy

Read More →

Applications of machine learning for imaging-driven diagnosis of musculoskeletal malignancies

European Radiology September 2022

Due to the rare nature of musculoskeletal malignancies and the lack of imaging data associated with this cancer, the authors of this study aimed to investigate whether machine learning (ML) is already having an impact on imaging-driven diagnosis of musculoskeletal malignancies. The authors concluded that because of the limited amounts of data and no established large-scale networks between multiple national

Read More →

Radiomic assessment of oesophageal adenocarcinoma

Insights into Imaging August 2022

The use of radiomic models offers a possible way to improve oesophageal adenocarcinoma assessment through quantitative image analysis, but model selection becomes complicated due to the myriad available predictors as well as the uncertainty of their relevance and reproducibility. Therefore, the aim of this study was to review recent research in order to facilitate precedent-based model selection for prospective validation

Read More →

Single-center versus multi-center biparametric MRI radiomics approach

Insights into Imaging June 2022

In this study, the authors’ aim was to investigate a previously developed radiomics-based biparametric magnetic resonance imaging (bpMRI) approach used for the discrimination of clinically significant peripheral zone prostate cancer (PZ csPCa) through the use of multi-center, multi-vendor (McMv) and single-center, single-vendor (ScSv) datasets. Using these datasets, the authors were able to determine that a single-center trained radiomics-based bpMRI model

Read More →

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Check out our different membership options.

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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
  • Exclusive option to participate in the European Diploma. 3
  • Free electronic access to the journal European Radiology 4
  • Content e-mails for all ESR journals
  • 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
  • Free electronic access to the journal European Radiology
  • Content e-mails for all 3 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.
③

ESR Friends

For students, company representatives or hospital managers etc.

  • Content e-mails for all 3 ESR journals 4
  • Updates on offers & events through our newsletters

€ 0

Friendship doesn’t cost a thing.
Apply now

The membership type best fitting for you will be selected automatically during the application process.

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.