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

Emphysema quantification using low-dose computed tomography with deep learning-based kernel conversion comparison

This study used a sample of 131 participants who underwent low-dose computed tomography (LDCT) and standard-dose computed tomography (SDCT) to determine the effect of dose reduction and kernel selection on quantifying emphysema. The authors determined that the deep learning-based CT kernel conversation of sharp kernel in LDCT significantly reduced the variation in emphysema quantification. Key points Low-dose computed tomography with

Read More →

Deep learning detection and quantification of pneumothorax in heterogeneous routine chest computed tomography

In this study, the authors proposed a deep learning method for the detection and quantification of pneumothorax in heterogeneous routine clinical data, which may facilitate the automated triage of urgent examinations and enable support in the treatment decision. Key points Pneumothorax is an important pathology to be included in applications that are designed to triage urgent imaging examinations. Heterogeneity in

Read More →

Deep learning for the determination of myometrial invasion depth and automatic lesion identification in endometrial cancer MR imaging

Endometrial cancer (EC) has the highest rate of malignancy in women in the entire world, including China, which has the largest population. Accurately staging EC prior to an invasive procedure still poses a challenge for clinicians. In the present study, we used more than five hundred EC patients’ MR images to train the computer to establish a deep learning diagnostic

Read More →

Optimal matrix size of chest radiographs for computer-aided detection on lung nodule or mass with deep learning

In this study, the authors retrospectively collected 2,088 abnormal and 352 normal chest radiographs from two institutions in order to investigate the optimal input matrix size for deep learning-based computer-aided detection (CAD) of nodules and masses on chest radiographs. This resulted in the matrix size 896 as having the highest performance for various sizes of abnormalities using different convolutional neural

Read More →

Deep learning for fully automated tumor segmentation and extraction of magnetic resonance radiomics features in cervical cancer

The purpose of this retrospective study was to develop and evaluate the performance of U-Net to determine whether U-Net-based deep learning could accurately perform fully automated localization and segmentation of cervical tumors in MR images, as well as the robustness of extracting apparent diffusion coefficient (ADC) radiomics features. Key points U-Net-based deep learning can perform accurate fully automated localization and

Read More →

Deep learning: definition and perspectives for thoracic imaging

The authors of this review aimed to provide definitions for understanding the methods of machine learning, deep learning, and convolutional neural networks (CNN) and to dive into their roles and potential in the area of thoracic imaging. Key points Deep learning outperforms other machine learning techniques for number of tasks in radiology. Convolutional neural network is the most popular deep

Read More →

Artificial intelligence for detecting small FDG-positive lung nodules in digital PET/CT

The authors of this study aimed to determine the diagnostic performance of a deep learning algorithm for the automated detection of small 18F-FDG-avid pulmonary nodules in positron emission tomography (PET) scans. Their findings suggest that these machine learning algorithms may be able to aid in the detection of small 18F-FDG-avid pulmonary nodules in clinical PET/CT scans, with artificial intelligence (AI)

Read More →

Accurate prediction of responses to transarterial chemoembolization for patients with hepatocellular carcinoma by using artificial intelligence in contrast-enhanced ultrasound

The aim of this study was to establish and validate an artificial intelligence-based radiomics strategy in order to predict personalized responses to hepatocellular carcinoma (HCC) to first transarterial chemoembolization (TACE) by analyzing contrast-enhanced ultrasound (CEUS) cines quantitatively. This was done using 130 HCC patients, showing that a deep learning-based radiomics method can effectively utilize CEUS, resulting in accurate and personalized

Read More →

Deep learning workflow in radiology: how to get started

In the past decade, deep learning architectures, which essentially consist of neural networks with numerous layers, have emerged as a dominant class of machine learning algorithms. Owing to the availability of larger datasets in radiology and access to high-performance graphical processing units, deep learning has provided state-of-the-art performance for various computer vision tasks such as lesion detection, segmentation, classification, monitoring,

Read More →

Dual-energy CT–based deep learning radiomics can improve lymph node metastasis risk prediction for gastric cancer

In this study, the authors aimed to build a dual-energy CT (DECT)-based deep learning radiomics nomogram that could be used for lymph node metastasis prediction in gastric cancer. Ultimately, the DECT-based deep learning radiomics nomogram operated well in predicting lymph node metastasis in gastric cancer. Key points This study investigated the value of deep learning dual-energy CT–based radiomics in predicting

Read More →

Emphysema quantification using low-dose computed tomography with deep learning-based kernel conversion comparison

This study used a sample of 131 participants who underwent low-dose computed tomography (LDCT) and standard-dose computed tomography (SDCT) to determine the effect of dose reduction and kernel selection on quantifying emphysema. The authors determined that the deep learning-based CT kernel conversation of sharp kernel in LDCT significantly reduced the variation in emphysema quantification. Key points Low-dose computed tomography with

Read More →

Deep learning detection and quantification of pneumothorax in heterogeneous routine chest computed tomography

In this study, the authors proposed a deep learning method for the detection and quantification of pneumothorax in heterogeneous routine clinical data, which may facilitate the automated triage of urgent examinations and enable support in the treatment decision. Key points Pneumothorax is an important pathology to be included in applications that are designed to triage urgent imaging examinations. Heterogeneity in

Read More →

Deep learning for the determination of myometrial invasion depth and automatic lesion identification in endometrial cancer MR imaging

Endometrial cancer (EC) has the highest rate of malignancy in women in the entire world, including China, which has the largest population. Accurately staging EC prior to an invasive procedure still poses a challenge for clinicians. In the present study, we used more than five hundred EC patients’ MR images to train the computer to establish a deep learning diagnostic

Read More →

Optimal matrix size of chest radiographs for computer-aided detection on lung nodule or mass with deep learning

In this study, the authors retrospectively collected 2,088 abnormal and 352 normal chest radiographs from two institutions in order to investigate the optimal input matrix size for deep learning-based computer-aided detection (CAD) of nodules and masses on chest radiographs. This resulted in the matrix size 896 as having the highest performance for various sizes of abnormalities using different convolutional neural

Read More →

Deep learning for fully automated tumor segmentation and extraction of magnetic resonance radiomics features in cervical cancer

The purpose of this retrospective study was to develop and evaluate the performance of U-Net to determine whether U-Net-based deep learning could accurately perform fully automated localization and segmentation of cervical tumors in MR images, as well as the robustness of extracting apparent diffusion coefficient (ADC) radiomics features. Key points U-Net-based deep learning can perform accurate fully automated localization and

Read More →

Deep learning: definition and perspectives for thoracic imaging

The authors of this review aimed to provide definitions for understanding the methods of machine learning, deep learning, and convolutional neural networks (CNN) and to dive into their roles and potential in the area of thoracic imaging. Key points Deep learning outperforms other machine learning techniques for number of tasks in radiology. Convolutional neural network is the most popular deep

Read More →

Artificial intelligence for detecting small FDG-positive lung nodules in digital PET/CT

The authors of this study aimed to determine the diagnostic performance of a deep learning algorithm for the automated detection of small 18F-FDG-avid pulmonary nodules in positron emission tomography (PET) scans. Their findings suggest that these machine learning algorithms may be able to aid in the detection of small 18F-FDG-avid pulmonary nodules in clinical PET/CT scans, with artificial intelligence (AI)

Read More →

Accurate prediction of responses to transarterial chemoembolization for patients with hepatocellular carcinoma by using artificial intelligence in contrast-enhanced ultrasound

The aim of this study was to establish and validate an artificial intelligence-based radiomics strategy in order to predict personalized responses to hepatocellular carcinoma (HCC) to first transarterial chemoembolization (TACE) by analyzing contrast-enhanced ultrasound (CEUS) cines quantitatively. This was done using 130 HCC patients, showing that a deep learning-based radiomics method can effectively utilize CEUS, resulting in accurate and personalized

Read More →

Deep learning workflow in radiology: how to get started

In the past decade, deep learning architectures, which essentially consist of neural networks with numerous layers, have emerged as a dominant class of machine learning algorithms. Owing to the availability of larger datasets in radiology and access to high-performance graphical processing units, deep learning has provided state-of-the-art performance for various computer vision tasks such as lesion detection, segmentation, classification, monitoring,

Read More →

Dual-energy CT–based deep learning radiomics can improve lymph node metastasis risk prediction for gastric cancer

In this study, the authors aimed to build a dual-energy CT (DECT)-based deep learning radiomics nomogram that could be used for lymph node metastasis prediction in gastric cancer. Ultimately, the DECT-based deep learning radiomics nomogram operated well in predicting lymph node metastasis in gastric cancer. Key points This study investigated the value of deep learning dual-energy CT–based radiomics in predicting

Read More →

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