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

Intelligent noninvasive meningioma grading using deep learning

The purpose of this study was to establish a robust interpretable deep learning (DL) model for the automatic noninvasive grading of meningiomas along with segmentation. Over 250 meningioma patients who underwent a preoperative brain MRI, including T2-weighted (T2) and contrast-enhanced T1-weighted (T1C) images, were included in the training set. The authors were able to determine that an interpretable multiparametric DL

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AI-aided software for detecting visible clinically significant prostate cancer on mpMRI

This study seeks to determine if artificial intelligence (AI)-based software can improve radiologists’ performance when detecting clinically significant prostate cancer. Sixteen radiologists from four hospitals participated and were assigned 30 cases, half without AI and half with AI. The authors determined that the AI software improves the performance of radiologists by reducing false positive detection of prostate cancer patients while

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Using a multi-decoder water-fat separation neural network for liver PDFF estimation

The authors of this study proposed a multi-task U-Net-based architecture to jointly estimate water-only and fat-only images. This approach allowed for the improvement in the estimation of water-fat images, enabling a reduction of the necessary echoes to achieve an accurate proton density fat fraction (PDFF) quantification. The proposed method was shown to be a reliable liver fat quantification tool for

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Hepatic steatosis increases liver volume

Our recent research published in European Radiology aimed to evaluate the impact of hepatic steatosis (HS) on liver volume by conducting a retrospective analysis of 1,038 living liver donors. We measured liver volume on gadoxetic acid-enhanced hepatobiliary phase MR images and proton density fat fraction (PDFF). Our results showed that HS leads to a 4.4% increase in liver volume per

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Radiologists’ performance improved by algorithmic transparency and interpretability measures

This study’s aim was to evaluate the perception of various types of artificial intelligence-based assistance and the interaction of radiologists with the algorithm’s predictions and certainty measures. As was consistent with previous research, the authors determined that human performance was superior to both groups when combined with AI. They also found an increase in trust in the algorithm’s performance when

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Machine learning and radiomics differentiate Crohn’s disease and ulcerative colitis

This retrospective study investigated whether volumetric visceral adipose tissue (VAT) features that were extracted using radiomics and three-dimensional convolutional neural network (3D-CNN) approaches are effective when differentiating Crohn’s disease (CD) and ulcerative colitis (UC). The authors concluded that VAT-based deep learning and radiomics features were able to achieve fair accuracy in differentiating CD from UC. Key points High-output feature data

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Covid-19 early detection: Neural Networks vs. Radiologists

The COVID-19 pandemic not only made an impact on the discipline of radiology as a whole but also on how we use specific tools in its detection. This was especially seen in the role of chest radiography when it was utilized as a diagnostic tool at the beginning of the pandemic “when microbiological resources were scarce,” evolving into its use

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Deep learning image reconstruction improves DECT image quality

The purpose of this phantom study was the compare the image quality of a deep learning image reconstruction (DLIR) algorithm and conventional iterative reconstruction (IR) algorithms in dual-energy CT (DECT) as well as assess the impact that these algorithms have on radiomics robustness. The authors determined that the new DLIR algorithm does in fact improve the quality of DECT images

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Automated vetting of radiology referrals: exploring NLP and ML approaches

As computed tomography (CT) sees an increase in utilization, inappropriate imaging has been seen as a significant concern; however, manual justification audits of radiology referrals are extremely time-consuming and carry a heavy financial burden. Therefore, the authors of this study aimed to retrospectively audit the justification of brain CT referrals by using natural language processing (NLP) and traditional machine learning

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T2-weighted MRI-based radiomics discriminati between benign and borderline epithelial ovarian tumors

The authors of this study investigated whether radiomics based on T2-weighted MRI was able to discriminate between benign and borderline epithelial ovarian tumors (EOTs) preoperatively. They were able to show that it can provide critical diagnostic information in the discrimination between benign and borderline EOTs, showing that there is potential to aid personalized treatment options. Key points T2-weighted MRI-based radiomics

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