neural networks

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

Super-resolution application of GAN on brain time-of-flight MR angiography

This study aimed to develop a generative adversarial network (GAN) model to improve the image resolution of brain time-of-flight MR angiography (TOF-MRA), as well as evaluate the image quality and diagnostic utility of the reconstructed images. The results showed that an optimized GAN could significantly improve the image quality and vessel visibility of low-resolution 3D TOF-MRA while maintaining equivalent sensitivity

Read More →

Non-invasive prediction of microsatellite instability in colorectal cancer

The goal of this study was to establish a CT-based radiomics signature in order to predict microsatellite instability (MSI) status in patients with colorectal cancer (CRC). Through data from 837 CRC patients from two hospitals who underwent preoperative enhanced CT and had available MSI status data, the authors were able to build a robust radiomics signature for the identification of

Read More →

How well does a 3D convolutional neural network perform in detecting hypoperfusion?

Due to the life-threatening nature of chronic pulmonary embolism (CPE) and how easily it can be misdiagnosed on computed tomography, the authors of this study investigated a three-dimensional convolutional neural network (CNN) algorithm for detecting hypoperfusion in CPE from computed tomography pulmonary angiography (CTPA). This study demonstrated the feasibility of a deep learning algorithm for detecting hypoperfusion in CPE from

Read More →

Clinically significant prostate cancer detection and segmentation in low-risk patients using a convolutional neural network on multi-parametric MRI

The purpose of this study was to develop an automatic method for the identification and segmentation of clinically significant prostate cancer in low-risk patients and evaluate this performance in a routine clinical setting. The authors discovered that the proposed deep learning computer-aided method showed promising results in the previously-mentioned identification and segmentation of clinically significant prostate cancer in patients on

Read More →

Multiphase CT-based prediction of Child-Pugh classification: a machine learning approach

The aim of this study was to evaluate whether machine learning algorithms allow for the prediction of Child-Pugh classification on clinical multiphase computed tomography (CT). The authors found that the performance of convolutional neural networks (CNN) is comparable to that of experienced radiologists in assessing Child-Pugh class based on multiphase abdominal CT. Key points Established machine learning algorithms can predict

Read More →

Automated volumetric assessment with artificial neural networks might enable a more accurate assessment of disease burden in patients with multiple sclerosis

Multiple automated methods for segmentation of multiple sclerosis (MS) lesions have been developed over the past years, and the use of artificial neural networks (ANN) has recently generated many outstanding results in the public segmentation challenges. As we all know from our work as radiologists, the routine clinical practice is always conducted with an economical balance between optimal scan times

Read More →

Long-term follow-up of persistent pulmonary pure ground-glass nodules with deep learning–assisted nodule segmentation

The aim of this study was to investigate the natural history of pulmonary pure ground-glass nodules (pGGNs) with deep learning (DL)-assisted nodule segmentation. The authors concluded that DL can assist in accurately explaining the natural history of pGGNs and that pGGNs with a lobulated sign and larger initial diameter, volume, and mass are more likely to grow. Key points The

Read More →

Fully automated convolutional neural network-based affine algorithm improves liver registration and lesion co-localization on hepatobiliary phase T1-weighted MR images

The authors of this study aimed to assess the performance of a convolutional neural network (CNN) algorithm to register cross-sectional liver imaging series and its performance to manual image registration. The study included three hundred fourteen patients who underwent gadoxetate disodium-enhanced magnetic resonance imaging (MRI) and were retrospectively selected. Key points Image registration across series can improve lesion co-localization and

Read More →

Application of a deep learning algorithm for detection and visualization of hip fractures on plain pelvic radiographs

In this study, we developed the DCNN not only for the automated detection of hip fractures on frontal pelvic radiographs but also to offer visualization of the fracture site by Grad-CAM, which enables the rapid integration of this tool into the current medical system. The age of artificial intelligence (AI) offers new opportunities but also poses challenges, for physicians. Deep

Read More →

Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT

Deep learning reconstruction (DLR) is a novel method of reconstruction that introduces deep convolutional neural networks into the reconstruction flow. The authors of this research examined the clinical applicability of abdominal ultra-high-resolution CT (U-HRCT) exams reconstructed with a new DLR and to compare it to hybrid and model-based iterative reconstruction (hybrid-IR, MBIR). Radiologists analysed and graded results from 46 patients

Read More →

Super-resolution application of GAN on brain time-of-flight MR angiography

This study aimed to develop a generative adversarial network (GAN) model to improve the image resolution of brain time-of-flight MR angiography (TOF-MRA), as well as evaluate the image quality and diagnostic utility of the reconstructed images. The results showed that an optimized GAN could significantly improve the image quality and vessel visibility of low-resolution 3D TOF-MRA while maintaining equivalent sensitivity

Read More →

Non-invasive prediction of microsatellite instability in colorectal cancer

The goal of this study was to establish a CT-based radiomics signature in order to predict microsatellite instability (MSI) status in patients with colorectal cancer (CRC). Through data from 837 CRC patients from two hospitals who underwent preoperative enhanced CT and had available MSI status data, the authors were able to build a robust radiomics signature for the identification of

Read More →

How well does a 3D convolutional neural network perform in detecting hypoperfusion?

Due to the life-threatening nature of chronic pulmonary embolism (CPE) and how easily it can be misdiagnosed on computed tomography, the authors of this study investigated a three-dimensional convolutional neural network (CNN) algorithm for detecting hypoperfusion in CPE from computed tomography pulmonary angiography (CTPA). This study demonstrated the feasibility of a deep learning algorithm for detecting hypoperfusion in CPE from

Read More →

Clinically significant prostate cancer detection and segmentation in low-risk patients using a convolutional neural network on multi-parametric MRI

The purpose of this study was to develop an automatic method for the identification and segmentation of clinically significant prostate cancer in low-risk patients and evaluate this performance in a routine clinical setting. The authors discovered that the proposed deep learning computer-aided method showed promising results in the previously-mentioned identification and segmentation of clinically significant prostate cancer in patients on

Read More →

Multiphase CT-based prediction of Child-Pugh classification: a machine learning approach

The aim of this study was to evaluate whether machine learning algorithms allow for the prediction of Child-Pugh classification on clinical multiphase computed tomography (CT). The authors found that the performance of convolutional neural networks (CNN) is comparable to that of experienced radiologists in assessing Child-Pugh class based on multiphase abdominal CT. Key points Established machine learning algorithms can predict

Read More →

Automated volumetric assessment with artificial neural networks might enable a more accurate assessment of disease burden in patients with multiple sclerosis

Multiple automated methods for segmentation of multiple sclerosis (MS) lesions have been developed over the past years, and the use of artificial neural networks (ANN) has recently generated many outstanding results in the public segmentation challenges. As we all know from our work as radiologists, the routine clinical practice is always conducted with an economical balance between optimal scan times

Read More →

Long-term follow-up of persistent pulmonary pure ground-glass nodules with deep learning–assisted nodule segmentation

The aim of this study was to investigate the natural history of pulmonary pure ground-glass nodules (pGGNs) with deep learning (DL)-assisted nodule segmentation. The authors concluded that DL can assist in accurately explaining the natural history of pGGNs and that pGGNs with a lobulated sign and larger initial diameter, volume, and mass are more likely to grow. Key points The

Read More →

Fully automated convolutional neural network-based affine algorithm improves liver registration and lesion co-localization on hepatobiliary phase T1-weighted MR images

The authors of this study aimed to assess the performance of a convolutional neural network (CNN) algorithm to register cross-sectional liver imaging series and its performance to manual image registration. The study included three hundred fourteen patients who underwent gadoxetate disodium-enhanced magnetic resonance imaging (MRI) and were retrospectively selected. Key points Image registration across series can improve lesion co-localization and

Read More →

Application of a deep learning algorithm for detection and visualization of hip fractures on plain pelvic radiographs

In this study, we developed the DCNN not only for the automated detection of hip fractures on frontal pelvic radiographs but also to offer visualization of the fracture site by Grad-CAM, which enables the rapid integration of this tool into the current medical system. The age of artificial intelligence (AI) offers new opportunities but also poses challenges, for physicians. Deep

Read More →

Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT

Deep learning reconstruction (DLR) is a novel method of reconstruction that introduces deep convolutional neural networks into the reconstruction flow. The authors of this research examined the clinical applicability of abdominal ultra-high-resolution CT (U-HRCT) exams reconstructed with a new DLR and to compare it to hybrid and model-based iterative reconstruction (hybrid-IR, MBIR). Radiologists analysed and graded results from 46 patients

Read More →

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