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

Using deep convolutional neural networks (dCNN) to mimic human decision-making

I believe artificial intelligence, more specifically deep convolutional neural networks (dCNN), could be used to classify US breast lesions. In our study, the implemented dCNN, with its robust sliding window approach, has demonstrated that high accuracies in the classification of US breast lesions can be reached using AI. In order to be integrated into the clinical workflow, classification strategies based

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European radiology needs AI luminaries

Medical imaging AI is progressing fast, boosted by an industrious community and technology advances. The market, however, is not big enough to support all the companies involved in the field and European radiologists still lack proper training, according to Simon Harris, principal analyst at Signify Research. What are the current market trends? The technology is starting to mature. It is

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Radiomics: a critical step towards integrated healthcare

This article aims to bring together the various technological developments that have taken place in medical imaging analysis and highlight a potential path for the future. While the term “medical image analysis” has classically referred to radiological images (CT, MRI, PET, etc.), we must also remember that digitalization occurred much earlier in other diagnostic disciplines like pathology (with pathomics) and

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Will AI replace radiologists? An interview with Professor Charles Kahn, Part II

The article below is a continuation of our interview with Professor Charles Kahn on the changing landscape of radiology when it comes to artificial intelligence (AI). In our previous article, Kahn discussed his thoughts on publishing in the field of AI, structuring articles, and data sharing. We continue now as our conversation changes course to the future of AI and

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Applying 3D CNN to CTA source images to detect ischemic stroke

In this study, the authors investigated how feasible it was to use 3D convolutional neural networks (CNN) to detect ischemic stroke from computed tomography angiography source images (CTA-SI). The study used CTA-SI from 60 randomly selected patients who had a suspected acute ischemic stroke of the middle cerebral artery; half of the patients were used in the neural network training,

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Using deep learning to detect and segment meningiomas

These days, selling a medical product or software solution without bringing up ‘artificial intelligence’ seems to be an almost impossible task. Hence, a certain mistrust by most radiologists is understandable and, sometimes, even warranted. Proof provided by well-conducted studies is, therefore, necessary to sort out the technical approaches and applications that could be truly useful, as well as trustworthy, for

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Responsible introduction of AI will help unleash its full potential

A myriad of tools is being created that use AI for medical imaging, but the community needs to be sure that the technology also works in clinical practice. This is only possible if AI is introduced in a responsible way, according to Wiro Niessen, a professor in Biomedical Image Analysis at Erasmus MC, Rotterdam and Delft University of Technology, and

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Radiomics: the facts and the challenges of image analysis

Radiomics is a complex multi-step process that can be considered as part of the more complex world of Artifical Intelligence (AI). The aim of radiomics is aiding clinical decision-making and outcome prediction for more personalized medicine. Each step of the radiomics process brings challenges that have to be considered; for example, segmentation is challenging because of reproducibility issues. Indeed, there

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Is there a future for deep learning-based generative models in musculoskeletal radiology?

This article sought to investigate the potential of generative models in the field of MRI of the spine, and did so by performing clinically relevant benchmark cases. The interest in generative models, which are computer programs that are able to generate novel data, as opposed to classifying or processing existing data, is due to the fact that considerable technological innovations

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