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

EuSoMII Radiomics Auditing Group Initiative: Review of RQS applications

When the Radiomics Quality Score (RQS) was presented to the scientific community back in 2017, its authors aimed to introduce a tool for a rapid and effective evaluation of radiomics studies’ scientific/clinical merit. Conceived as a quality seal to be published alongside presented results and newly proposed radiomics models, the RQS has instead mostly been adopted by researchers as a

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Standardised lesion segmentation for imaging biomarker quantitation

Extraction of quantitative data from images requires selecting the regions-of-interest from which this data is to be extracted. This process of region-of-interest selection (or segmentation), whether it be manual or automated, crucially determines the values of the extracted biomarkers. Harmonisation of the segmentation process itself is therefore central to the standardisation of imaging biomarkers. We undertook a Delphi process amongst

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Prediction of lipomatous soft tissue malignancy on MRI

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

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Opportunistic screening for osteoporosis and osteopenia using machine learning

Dr. Rizwan Aslam, of the University of California, San Francisco (UCSF), presented an abstract at RSNA in 2008 which showed that it was possible to screen for osteoporosis from CT colonoscopy scans while I was a resident at UCSF. Subsequently, other papers showed that there were strong correlations between CT measurements and the measurements obtained from dual-energy x-ray absorptiometry (DXA).

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Application of deep learning reconstruction in the diagnosis of renal calculi

In this study, the authors evaluated the diagnostic capability, image quality, and radiation dose of abdominal ultra-low-dose CT (ULDCT) with deep learning reconstruction (DLR) in the detection of renal calculi, which are a common urological disease and normally detected by CT. They were able to find that ULDCT combined with DLR could significantly reduce radiation dose, decrease image noise, and

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Using parametric feature maps to enhance the stability of CT radiomics

It is known that the reproducibility of radiomic features is influenced by myriad factors, one of which is the size of the segmented volume. We hypothesized that parametric maps calculated with a fixed voxel size could address this issue. To test the hypothesis, we conducted a phantom study and could show that the stability across different volumes of interest sizes

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Cancer-associated incidental pulmonary embolism – underreporting and the potential role of AI

Pulmonary embolism (PE) is a common complication in patients with cancer. A significant number of all PE are diagnosed incidentally (incidental PE, iPE) in CT scans performed for staging or treatment response evaluation. Thousands of CT scans are performed for this indication every year in Halmstad, a regional hospital in Region Halland in southern Sweden, and we anticipate that in

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Deep learning image reconstruction to improve accuracy of iodine quantification and image quality in abdomen dual-energy CT

The aim of this study was to investigate the effect of deep learning image reconstruction (DLIR) on the accuracy of iodine quantification and image quality of dual-energy CT (DECT) compared to other reconstruction algorithms. The authors showed that the DLIR algorithm reduced image noise and variability of iodine concentration values when compared with other reconstruction algorithms. Key points In the

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Reproducibility of AI models in head CT

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

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The role of radiomics in the detection of lymph node metastases

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

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