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

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

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

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Evaluating Radiomics Research Reporting Assessment Tools to Improve Quality and Generalizability

As computational capabilities in healthcare continue to advance, the realm of texture analysis within medical imaging, known as radiomics, offers a promising avenue for uncovering novel imaging biomarkers aiding precision medicine [1].Nevertheless, clinical translation faces significant hurdles, primarily stemming from the heterogeneity of research questions and inconsistent quality of radiomics reporting, leading to a scarcity of studies that are comparable,

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Complexities of deep learning-based undersampled MR image reconstruction

Recent advances in AI have led to deep learning-based MR undersampled image reconstruction methods showing more speed-ups compared to traditional algorithms. MR undersampling is an excellent way to reduce scan time but can negatively impact image quality. Our literature review aims to inform a broader audience about this complex topic. This highly multidisciplinary science requires the informed input of many

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Unleashing the power of data in radiology: A look ahead

Peering into the future of radiology, we find ourselves at an exciting crossroads. The past year has seen language-based foundation models disrupt the status quo, offering a tantalising glimpse into new possibilities for data accessibility. Yet we still face a huge challenge: a wealth of valuable data remains locked away in free-text reports. But what if we could unlock this

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Predicting tertiary lymphoid structures status of ICC patients using CT radiomics

The authors of this study used preoperative CT radiomics in order to predict the tertiary lymphoid structures (TLSs) status and recurrence-free survival (RFS) of intrahepatic cholangiocarcinoma (ICC) patients. Enhanced CT images from a total of 116 ICC patients were included when using the radiomics model. The study results showed that the radiomics nomogram displayed better performance in predicting TLSs than

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Ready for testing artificial intelligence in radiology clinical practice

In the near future, I believe AI support systems will play a crucial role in the daily practice of radiologic reading. Radiologists are faced with an ever-increasing number of examinations and it remains vital to identify urgent cases as quickly as possible. An initial certified support system on the market has reported interesting performance data that proved the positive effect

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Reproducibility of radiomics quality score: an intra- and inter-rater reliability study

The rapidly evolving field of radiomics research holds the potential to revolutionize medicine by transforming diagnostic images into quantifiable data. Assessing the quality of radiomics research is challenging and requires reliable tools for standardization. Our results, published in European Radiology, have revealed some room for improvement regarding the reproducibility of the widely used Radiomics Quality Score (RQS). We recruited multiple

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MRI-based radiomics models show promise in clinical decision-making in spinal metastases patients

The aim of this study was to extract radiomics features from MRI using various machine learning algorithms which would then be integrated with clinical features to build response prediction models for spinal metastases patients who were undergoing stereotactic body radiotherapy (SBRT). The authors found that the MRI-based radiomics models showed valuable predictive capability for treatment outcomes regarding spinal metastases patients

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Implementing AI in breast imaging: challenges to turn the gadget into gain

In healthcare, the implementation of artificial intelligence (AI) is rapidly gaining momentum with breast imaging being no exception, or rather a poster child case. Numerous clinical indications intuitively lend themselves to AI enhancement. While the adoption of broad AI in clinical breast imaging practice has been more or less a silent revolution – it is already widely used for invite

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New radiomics model to predict the Leibovich risk groups for ccRCC patients

This study developed and validated a triphasic CT-based radiomics model, which incorporated radiomics features and significant clinical factors, for preoperative risk stratification of patients with localized clear cell renal cell carcinoma (ccRCC). The model showed a favorable performance in preoperatively predicting the Leibovich low-risk and intermediate-high-risk groups in localized ccRCC patients. The authors determined that this model can be used

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