AI Blog

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.

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Latest posts

Study compares robotic CT-guided out-of-plane needle insertion with smartphone-guided application

The authors in this study aimed to evaluate the accuracy of robotic CT-guided out-of-plane needle insertion using phantom and animal experiments. A robotic system, Zerobot, which was developed at the authors’ institution, was used for needle insertion. Overall, there were 12 robotic needle insertions which were then compared with the same insertions performed by hand as well as guided by

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Is Digitalization Driving Innovation or Innovation Digitalization?

In medical imaging, digitalization can be like the wheel is to cars and like the inclined plane is to construction, or it can satisfy needs. This can relate to either technology or workflow – or both. Neither MRI nor CT imaging could exist without digitalization. But determining whether digitalization is the actual technology or rather enabled its development is a

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AI shows potential in low-resource settings

The need for diagnostic images is rapidly exceeding the capacity of available specialists worldwide, and more acutely so in developing countries where access to healthcare remains challenging. AI is showing promise in TB diagnosis, but it could do much more, provided incentives went in the right direction, recent initiatives in Uganda have shown. Shortage of radiologists is a worldwide phenomenon,

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Multiparametric MRI and auto-fixed volume of interest-based radiomics signature for clinically significant peripheral zone prostate cancer

The purpose of this study was to create a radiomics approach based on multiparametric MRI (mpMRI) features that were extracted from an auto-fixed volume of interest (VOI) that quantifies the phenotype of clinically significant peripheral zone prostate cancer (pCA). The study included 206 patients and the authors concluded that the developed radiomics model that extracts mpMRI features with an auto-fixed

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Making AI Accessible

What are the practical applications of Artificial Intelligence in healthcare? Dr. Christoph Zindel, who is a member of the Managing Board of Siemens Healthineers and responsible for the Imaging and Advanced Therapies business segments, as well as for technology and innovation, shares his vision in an interview with Healthcare IT News. To learn more, click here.

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

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

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Long-term follow-up of persistent pulmonary pure ground-glass nodules with deep learning–assisted nodule segmentation

The aim of this study was to investigate the natural history of pulmonary pure ground-glass nodules (pGGNs) with deep learning (DL)-assisted nodule segmentation. The authors concluded that DL can assist in accurately explaining the natural history of pGGNs and that pGGNs with a lobulated sign and larger initial diameter, volume, and mass are more likely to grow. Key points The

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The cost of AI in radiology: is it really worth it?

How much do you need to spend to use artificial intelligence (AI) in radiology? Between software, hardware and infrastructure, expenses can add up quickly. And without a reimbursement scheme, it takes more than just money to advance the field. One last price to pay could well be AI’s impact on the environment, as training a single algorithm emits a significant

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

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.

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You will have access to a wide range of benefits that can help you advance your career and stay up-to-date with the latest developments in the field of radiology. These benefits include access to educational resources, networking opportunities with other professionals in the field, opportunities to participate in research projects and clinical trials, and access to the latest technologies and techniques. 

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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

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  • Reduced registration fees for ECR 1
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  • 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.