deep learning

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

Label-set impact on deep learning-based prostate segmentation on MRI

This study delves into a less-explored territory in automatic prostate segmentation: label-set selection. Recognizing the emphasis on dataset selection in segmentation model training, we thought it crucial to investigate the impact of the labels, i.e. the manual segmentations, on model performance. Although label sets are often considered the gold standard, as they are provided by highly trained professionals, disparities emerge

Read More →

External validation, radiological evaluation, and development of DL lung segmentation in chest CT

The authors of this study developed a 3D nnU-Net-based model for automatic lung segmentation in computed tomography pulmonary angiography (CTPA) imaging that was found to be highly accurate, clinically evaluated, and externally tested in patient cohorts with a spread of lung disease. Key points Article: External validation, radiological evaluation, and development of deep learning automatic lung segmentation in contrast-enhanced chest CT

Read More →

Deep learning–based identification of spine growth potential on EOS radiographs

In this study, the authors developed a deep learning-based algorithm, which is able to mimic human judgment, in order to help clinicians assess the potential of spine growth based on EOS radiographs. The outcome of the study showed that their deep learning method achieved comparable, and even superior, results compared to those of clinicians, which should have positive applications in

Read More →

Does deep learning improve the consistency and performance of radiologists in assessing bi-parametric prostate MRI?

Our study aimed to evaluate whether deep learning (DL) software could enhance the consistency and performance of radiologists in assessing bi-parametric prostate MRI scans. Intriguingly, our findings revealed that the DL software did not significantly improve the Prostate Imaging-Reporting and Data System (PI-RADS) scoring consistency or the detection performance of clinically significant prostate cancer (csPCa) among radiologists with varying levels

Read More →

Are deep models in radiomics performing better than generic models?

In radiomics, deep learning methods are increasingly used because they promise higher predictive performance than models based on generic, hand-crafted features. However, sample sizes in radiomics are often small, and it is known that the performance of deep learning models is often critically dependent on sample size. Therefore, it is unclear whether deep models can outperform generic models. In our

Read More →

A deep learning model using chest X-ray to identify TB and NTM-LD patients

The authors of this study aimed to evaluate whether artificial intelligence, specifically a deep neural network (DNN), was able to distinguish between tuberculosis (TB) or nontuberculous mycobacterial lung disease (NTM-LD) patients through chest X-rays (CXRs) from suspected mycobacterial lung disease. A total of 1,500 CXRs from two hospitals were retrospectively collected and evaluated. They determined that the developed DNN model

Read More →

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

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

Read More →

Multi-channel deep learning model diagnoses the cause of LVH

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

Read More →

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

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

Read More →

A novel AI model to distinguish benign from malignant ovarian tumors

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

Read More →

Label-set impact on deep learning-based prostate segmentation on MRI

This study delves into a less-explored territory in automatic prostate segmentation: label-set selection. Recognizing the emphasis on dataset selection in segmentation model training, we thought it crucial to investigate the impact of the labels, i.e. the manual segmentations, on model performance. Although label sets are often considered the gold standard, as they are provided by highly trained professionals, disparities emerge

Read More →

External validation, radiological evaluation, and development of DL lung segmentation in chest CT

The authors of this study developed a 3D nnU-Net-based model for automatic lung segmentation in computed tomography pulmonary angiography (CTPA) imaging that was found to be highly accurate, clinically evaluated, and externally tested in patient cohorts with a spread of lung disease. Key points Article: External validation, radiological evaluation, and development of deep learning automatic lung segmentation in contrast-enhanced chest CT

Read More →

Deep learning–based identification of spine growth potential on EOS radiographs

In this study, the authors developed a deep learning-based algorithm, which is able to mimic human judgment, in order to help clinicians assess the potential of spine growth based on EOS radiographs. The outcome of the study showed that their deep learning method achieved comparable, and even superior, results compared to those of clinicians, which should have positive applications in

Read More →

Does deep learning improve the consistency and performance of radiologists in assessing bi-parametric prostate MRI?

Our study aimed to evaluate whether deep learning (DL) software could enhance the consistency and performance of radiologists in assessing bi-parametric prostate MRI scans. Intriguingly, our findings revealed that the DL software did not significantly improve the Prostate Imaging-Reporting and Data System (PI-RADS) scoring consistency or the detection performance of clinically significant prostate cancer (csPCa) among radiologists with varying levels

Read More →

Are deep models in radiomics performing better than generic models?

In radiomics, deep learning methods are increasingly used because they promise higher predictive performance than models based on generic, hand-crafted features. However, sample sizes in radiomics are often small, and it is known that the performance of deep learning models is often critically dependent on sample size. Therefore, it is unclear whether deep models can outperform generic models. In our

Read More →

A deep learning model using chest X-ray to identify TB and NTM-LD patients

The authors of this study aimed to evaluate whether artificial intelligence, specifically a deep neural network (DNN), was able to distinguish between tuberculosis (TB) or nontuberculous mycobacterial lung disease (NTM-LD) patients through chest X-rays (CXRs) from suspected mycobacterial lung disease. A total of 1,500 CXRs from two hospitals were retrospectively collected and evaluated. They determined that the developed DNN model

Read More →

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

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

Read More →

Multi-channel deep learning model diagnoses the cause of LVH

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

Read More →

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

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

Read More →

A novel AI model to distinguish benign from malignant ovarian tumors

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

Read More →

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