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Back to main AI Blog

deep 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:

deep learningmachine learningmriradiomicsx-ray computed tomographyinterviewai newsmammographyCOVID-19breast cancerneural networksdiagnostic imagingtomographyworkflowradiographyMDCT

Latest posts

Deep learning-based algorithm vs. Virtual monoenergetic imaging and orthopedic metal artifact reduction

European Radiology Experimental October 2024

This study compared the image quality, metal artifacts, and diagnostic confidence of conventional CT images of unilateral total hip arthroplasty patients (THA) with deep learning-based metal artifact reduction (DL-MAR) to conventional CT and 130-keV monoenergetic images with and without orthopedic metal artifact reduction (O-MAR). They found that DL-MAR showed not only higher image quality but also diagnostic confidence and superior

Read More →

Automatic segmentation of fat metaplasia on sacroiliac joint MRI using deep learning

Insights into Imaging September 2024

The authors of this study developed a deep learning model used for segmenting fat metaplasia (FM) on sacroiliac joint (SIJ) MRI with the additional aim of utilizing the deep learning model to classify axial spondyloarthritis (axSpA) and non-axSpA. They were able to conclude that the deep learning model could automatically and accurately segment FM on SIJ MRI, helping to increase

Read More →

AI applied to MRI reliably detects the presence of meniscus tears

European Radiology August 2024

It is known that meniscus tears are difficult to diagnose on knee MRIs. Therefore, this study reviews and compares the accuracy of convolutional neural networks (CNNs). The authors assessed databases including PubMed, MEDLINE, EMBASE, and Cochrane, finding eleven articles to include in the final review, consisting of over 13,000 patients and over 57,000 images. They concluded that CNN is accurate

Read More →

Detection of femoropopliteal arterial steno-occlusion at MR angiography

European Radiology Experimental August 2024

This single-centre retrospective study aimed to evaluate a deep learning (DL) algorithm for detecting vessel steno-occlusions in peripheral arterial disease patients (PAD). The authors’ findings suggest that the proposed DL model is promising and an effective tool to assist in the detection of arterial steno-occlusions in PAD patients. Key points: Article: Detection of femoropopliteal arterial steno-occlusion at MR angiography: initial

Read More →

AI-based algorithm offers reliable Cobb angle measurement on routine MRI for degenerative scoliosis patients

European Radiology July 2024

Due to how the severity of degenerative scoliosis is assessed, this retrospective study aimed to develop and evaluate the reliability of a novel automatic method that measured Cobb angles on lumbar MRI in degenerative scoliosis (DS) patients. The authors developed a 3D artificial intelligence algorithm that was trained on 447 lumbar MRI. The study concluded that the AI-based algorithm offered

Read More →

Shallow and deep learning classifiers in medical image analysis

European Radiology Experimental July 2024

The authors of this review aimed to give educational insight into the most accessible and widely employed classifiers in the field of radiology, distinguish between “shallow” learning algorithms, as well as look into “deep” learning architectures such as convolutional neural networks and vision transformers. This review found that machine learning classifiers offer vital information for the development of clinical decision

Read More →

Deep learning-based 3D cerebrovascular segmentation workflow on bright and black blood sequences MRA

Insights into Imaging July 2024

Cerebrovascular diseases are seen as a significant threat to human life and health, and the segmentation of brain blood vessels has become a scientific challenge. Therefore, the authors of this study aimed to develop a fully automated deep learning workflow capable of accurate 3D segmentation of cerebral blood vessels using convolutional neural networks (CNNs) and transformer models. The study, conducted

Read More →

Knee landmarks detection via deep learning

European Radiology June 2024

A deep learning-based approach was developed and validated in this study which aimed to automatically measure the patellofemoral instability (PFI) indices related to patellar height and trochlear dysplasia in knee MRI scans. The authors included a total of 763 knee MRI slices from 95 patients, annotating 3,393 anatomical landmarks. The results indicated that the developed models achieved good accuracy in

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 →

A deep learning framework for intracranial aneurysms automatic segmentation and detection on magnetic resonance T1 images

European Radiology May 2024

This study featured a design of a deep learning-based framework for the automatic segmentation of intracranial aneurysms (IAs) on MR T1 images while also testing the robustness and performance of the framework. The authors were able to conclude that their deep learning framework could effectively detect and segment IAs using clinical routine T1 sequences, which offers potential in improving the

Read More →

Deep learning-based algorithm vs. Virtual monoenergetic imaging and orthopedic metal artifact reduction

European Radiology Experimental October 2024

This study compared the image quality, metal artifacts, and diagnostic confidence of conventional CT images of unilateral total hip arthroplasty patients (THA) with deep learning-based metal artifact reduction (DL-MAR) to conventional CT and 130-keV monoenergetic images with and without orthopedic metal artifact reduction (O-MAR). They found that DL-MAR showed not only higher image quality but also diagnostic confidence and superior

Read More →

Automatic segmentation of fat metaplasia on sacroiliac joint MRI using deep learning

Insights into Imaging September 2024

The authors of this study developed a deep learning model used for segmenting fat metaplasia (FM) on sacroiliac joint (SIJ) MRI with the additional aim of utilizing the deep learning model to classify axial spondyloarthritis (axSpA) and non-axSpA. They were able to conclude that the deep learning model could automatically and accurately segment FM on SIJ MRI, helping to increase

Read More →

AI applied to MRI reliably detects the presence of meniscus tears

European Radiology August 2024

It is known that meniscus tears are difficult to diagnose on knee MRIs. Therefore, this study reviews and compares the accuracy of convolutional neural networks (CNNs). The authors assessed databases including PubMed, MEDLINE, EMBASE, and Cochrane, finding eleven articles to include in the final review, consisting of over 13,000 patients and over 57,000 images. They concluded that CNN is accurate

Read More →

Detection of femoropopliteal arterial steno-occlusion at MR angiography

European Radiology Experimental August 2024

This single-centre retrospective study aimed to evaluate a deep learning (DL) algorithm for detecting vessel steno-occlusions in peripheral arterial disease patients (PAD). The authors’ findings suggest that the proposed DL model is promising and an effective tool to assist in the detection of arterial steno-occlusions in PAD patients. Key points: Article: Detection of femoropopliteal arterial steno-occlusion at MR angiography: initial

Read More →

AI-based algorithm offers reliable Cobb angle measurement on routine MRI for degenerative scoliosis patients

European Radiology July 2024

Due to how the severity of degenerative scoliosis is assessed, this retrospective study aimed to develop and evaluate the reliability of a novel automatic method that measured Cobb angles on lumbar MRI in degenerative scoliosis (DS) patients. The authors developed a 3D artificial intelligence algorithm that was trained on 447 lumbar MRI. The study concluded that the AI-based algorithm offered

Read More →

Shallow and deep learning classifiers in medical image analysis

European Radiology Experimental July 2024

The authors of this review aimed to give educational insight into the most accessible and widely employed classifiers in the field of radiology, distinguish between “shallow” learning algorithms, as well as look into “deep” learning architectures such as convolutional neural networks and vision transformers. This review found that machine learning classifiers offer vital information for the development of clinical decision

Read More →

Deep learning-based 3D cerebrovascular segmentation workflow on bright and black blood sequences MRA

Insights into Imaging July 2024

Cerebrovascular diseases are seen as a significant threat to human life and health, and the segmentation of brain blood vessels has become a scientific challenge. Therefore, the authors of this study aimed to develop a fully automated deep learning workflow capable of accurate 3D segmentation of cerebral blood vessels using convolutional neural networks (CNNs) and transformer models. The study, conducted

Read More →

Knee landmarks detection via deep learning

European Radiology June 2024

A deep learning-based approach was developed and validated in this study which aimed to automatically measure the patellofemoral instability (PFI) indices related to patellar height and trochlear dysplasia in knee MRI scans. The authors included a total of 763 knee MRI slices from 95 patients, annotating 3,393 anatomical landmarks. The results indicated that the developed models achieved good accuracy in

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 →

A deep learning framework for intracranial aneurysms automatic segmentation and detection on magnetic resonance T1 images

European Radiology May 2024

This study featured a design of a deep learning-based framework for the automatic segmentation of intracranial aneurysms (IAs) on MR T1 images while also testing the robustness and performance of the framework. The authors were able to conclude that their deep learning framework could effectively detect and segment IAs using clinical routine T1 sequences, which offers potential in improving the

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

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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

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  • Updates on offers & events through our newsletters

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Footnotes:

01

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

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.