deep learning

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Evaluation of techniques to improve a deep learning algorithm for the automatic detection of intracranial haemorrhage on CT head imaging

European Radiology Experimental September 2023

This study evaluates deep learning (DL) algorithms that are playing an increasingly important role in automatic medical image analysis. The DL algorithm used was trained and externally evaluated on open-source, multi-centre retrospective data that contained radiologist-annotated non-contrast CT head studies. The authors concluded that the DL model has applications in a triage role with the potential to improve diagnostic yield

Multi-channel deep learning model diagnoses the cause of LVH

Insights into Imaging September 2023

A new study sees the development of a fully automatic framework for the diagnosis of the cause of left ventricular hypertrophy (LVH) via cardiac cine images. The fully automatic myocardium segmentation and spatial-temporal morphology feature-based LVH etiology diagnosis deep learning framework model was able to show a favorable and robust performance in diagnosing the cause of LVH, which could be

Evaluating a deep learning software for lung parenchyma characterization in COVID-19 pneumonia

European Radiology Experimental August 2023

The aim of this study was to evaluate the performance of the LungQuant system, which is a deep learning-based software for quantitative analysis of chest CT. LungQuant was evaluated by comparing its results with independent visual evaluations by a group of clinical experts. The results indicated that an automatic quantification tool may be beneficial and contribute to an improved clinical

A novel AI model to distinguish benign from malignant ovarian tumors

Insights into Imaging July 2023

The authors of this study developed a CT-based artificial intelligence model with the ability to differentiate between benign and malignant ovarian tumors, showing high accuracy and specificity. In coordination with less-experienced radiologists, the model helped in the performance of ovarian tumor assessment, with applications to provide better therapeutic strategies for patients with ovarian tumors. Key points CT-based radiomics and deep

Intelligent noninvasive meningioma grading using deep learning

European Radiology July 2023

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

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

Insights into Imaging July 2023

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

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

European Radiology June 2023

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

Hepatic steatosis increases liver volume

Seung Soo Lee June 2023

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

Covid-19 early detection: Neural Networks vs. Radiologists

Insights into Imaging May 2023

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

Deep learning image reconstruction improves DECT image quality

European Radiology May 2023

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

Evaluation of techniques to improve a deep learning algorithm for the automatic detection of intracranial haemorrhage on CT head imaging

European Radiology Experimental September 2023

Welcome to the blog on Artificial Intelligence of
the European Society of Radiology

Latest posts

Evaluation of techniques to improve a deep learning algorithm for the automatic detection of intracranial haemorrhage on CT head imaging

Multi-channel deep learning model diagnoses the cause of LVH

Evaluating a deep learning software for lung parenchyma characterization in COVID-19 pneumonia

A novel AI model to distinguish benign from malignant ovarian tumors

Intelligent noninvasive meningioma grading using deep learning

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

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

Hepatic steatosis increases liver volume

Covid-19 early detection: Neural Networks vs. Radiologists

Deep learning image reconstruction improves DECT image quality

Evaluation of techniques to improve a deep learning algorithm for the automatic detection of intracranial haemorrhage on CT head imaging

Multi-channel deep learning model diagnoses the cause of LVH

Evaluating a deep learning software for lung parenchyma characterization in COVID-19 pneumonia

A novel AI model to distinguish benign from malignant ovarian tumors

Intelligent noninvasive meningioma grading using deep learning

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

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

Hepatic steatosis increases liver volume

Covid-19 early detection: Neural Networks vs. Radiologists

Deep learning image reconstruction improves DECT image quality

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deep learning

Welcome to the blog on Artificial Intelligence ofthe European Society of Radiology

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Check out our different membership options.

Membership

Free membership

ESR Friends

Footnotes:

Welcome to the blog on Artificial Intelligence of
the European Society of Radiology