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

Artificial intelligence for prediction of COVID-19 progression using CT imaging and clinical data

Although challenging to predict, early recognition of COVID-19 severity can help guide patient management. The authors of this study aimed to develop an artificial intelligence system that was capable of predicting future deterioration to critical illness in COVID-19 patients. The AI system was developed to integrate chest CT and clinical data for risk prediction of said future deterioration to critical

Read More →

Deep learning–based algorithm to detect primary hepatic malignancy in multiphase CT of patients at high risk for HCC

Of late, deep learning-based algorithms have been successfully applied to various medical imaging modalities, ranging from chest radiographs to head CT scans. Compared to other body parts, there is a paucity of data regarding the application of deep learning-based algorithms in the liver. This can be attributed to the following reasons: First, unlike other body parts usually relying on single-phase

Read More →

Opportunistic osteoporosis screening in multi-detector CT images using deep convolutional neural networks

In this study, the authors explored the application of deep learning in patients with primary osteoporosis. Furthermore, they aimed to develop a fully automatic method based on a deep convolutional neural network (DCNN) for vertebral body segmentation and bone mineral density (BMD) calculation in CT images. The authors were able to determine that a deep learning-based method could achieve full

Read More →

How to read and review papers on machine learning and artificial intelligence in radiology: a survival guide to key methodological concepts

In this article, the authors aimed to guide and inform the radiology community regarding key methodological aspects of machine learning (ML) in order to improve their academic reading and peer-review experience. This was done so within four broad categories: study design, data handling, modelling, and reporting. Key points Machine learning is new and rather complex for the radiology community. Validity,

Read More →

Diagnostic performance for pulmonary adenocarcinoma on CT: comparison of radiologists with and without three-dimensional convolutional neural network

Convolutional neural networks (CNNs) are often used in the area of image recognition. We constructed the three dimensional (3D)-CNN model to predict pulmonary invasive adenocarcinoma (IVA) in this study. When supported by the 3D-CNN model, a less-experienced radiologist showed improved diagnostic accuracy for diagnosing IVA without deteriorating any diagnostic performances, resulting in the increase in the sensitivity of IVA diagnosis

Read More →

Noise reduction approach in pediatric abdominal CT combining deep learning and dual-energy technique

The authors of this study aimed to evaluate the image quality of low iodine concentration, dual-energy CT (DECT) combined with a deep learning-based noise reduction technique for pediatric abdominal CT, compared with standard iodine concentration single-energy polychromatic CT (SECT). They were able to determine that low iodine concentration DECT, when combined with deep learning in pediatric abdominal CT, can maintain

Read More →

Deep learning–based automated detection algorithm for active pulmonary tuberculosis on chest radiographs: diagnostic performance in systematic screening of asymptomatic individuals

Chest radiographs (CRs) have long been used as one of the screening tests for pulmonary tuberculosis (TB). However, the interpretation of a large number of CRs is time-consuming and labor-intensive. To overcome this difficulty, we developed the deep-learning-based automated detection (DLAD) for active pulmonary TB detection and performed out-of-sample testing in the consecutively collected 20.135 CRs from 19.686 servicepersons. As

Read More →

Simulated clinical deployment of fully automatic deep learning for clinical prostate MRI assessment

We use a previously validated artificial neural network to evaluate its performance in a much larger, subsequent, consecutive cohort. In the community, there exists a belief that with infinite training data, an AI system can theoretically be trained that has the ability to handle all possible data and thus be generalised to all environments. Applied to the prostate, this would

Read More →

From community-acquired pneumonia to COVID-19: a deep learning–based method for quantitative analysis of COVID-19 on thick-section CT scans

In this retrospective study, the authors aimed to develop a fully automated artificial intelligence (AI) system to quantitatively assess the severity and progression of COVID-19 using thick-section chest CT images. Through their research and work, they were able to determine that a deep learning-based AI system built on thick-section CT imaging can accurately quantify COVID-19-associated abnormalities in the lung and

Read More →

Automated identification of chest radiographs with referable abnormality with deep learning: need for recalibration

The purpose of this study was to evaluate the calibration of a deep learning (DL) model in a diagnostic cohort, as well as to improve the model’s calibration through recalibration procedures. The authors found that the calibration of the DL algorithm can be augmented through simple recalibration procedures, and improved calibration may enhance the interpretability and credibility of the model

Read More →

Artificial intelligence for prediction of COVID-19 progression using CT imaging and clinical data

Although challenging to predict, early recognition of COVID-19 severity can help guide patient management. The authors of this study aimed to develop an artificial intelligence system that was capable of predicting future deterioration to critical illness in COVID-19 patients. The AI system was developed to integrate chest CT and clinical data for risk prediction of said future deterioration to critical

Read More →

Deep learning–based algorithm to detect primary hepatic malignancy in multiphase CT of patients at high risk for HCC

Of late, deep learning-based algorithms have been successfully applied to various medical imaging modalities, ranging from chest radiographs to head CT scans. Compared to other body parts, there is a paucity of data regarding the application of deep learning-based algorithms in the liver. This can be attributed to the following reasons: First, unlike other body parts usually relying on single-phase

Read More →

Opportunistic osteoporosis screening in multi-detector CT images using deep convolutional neural networks

In this study, the authors explored the application of deep learning in patients with primary osteoporosis. Furthermore, they aimed to develop a fully automatic method based on a deep convolutional neural network (DCNN) for vertebral body segmentation and bone mineral density (BMD) calculation in CT images. The authors were able to determine that a deep learning-based method could achieve full

Read More →

How to read and review papers on machine learning and artificial intelligence in radiology: a survival guide to key methodological concepts

In this article, the authors aimed to guide and inform the radiology community regarding key methodological aspects of machine learning (ML) in order to improve their academic reading and peer-review experience. This was done so within four broad categories: study design, data handling, modelling, and reporting. Key points Machine learning is new and rather complex for the radiology community. Validity,

Read More →

Diagnostic performance for pulmonary adenocarcinoma on CT: comparison of radiologists with and without three-dimensional convolutional neural network

Convolutional neural networks (CNNs) are often used in the area of image recognition. We constructed the three dimensional (3D)-CNN model to predict pulmonary invasive adenocarcinoma (IVA) in this study. When supported by the 3D-CNN model, a less-experienced radiologist showed improved diagnostic accuracy for diagnosing IVA without deteriorating any diagnostic performances, resulting in the increase in the sensitivity of IVA diagnosis

Read More →

Noise reduction approach in pediatric abdominal CT combining deep learning and dual-energy technique

The authors of this study aimed to evaluate the image quality of low iodine concentration, dual-energy CT (DECT) combined with a deep learning-based noise reduction technique for pediatric abdominal CT, compared with standard iodine concentration single-energy polychromatic CT (SECT). They were able to determine that low iodine concentration DECT, when combined with deep learning in pediatric abdominal CT, can maintain

Read More →

Deep learning–based automated detection algorithm for active pulmonary tuberculosis on chest radiographs: diagnostic performance in systematic screening of asymptomatic individuals

Chest radiographs (CRs) have long been used as one of the screening tests for pulmonary tuberculosis (TB). However, the interpretation of a large number of CRs is time-consuming and labor-intensive. To overcome this difficulty, we developed the deep-learning-based automated detection (DLAD) for active pulmonary TB detection and performed out-of-sample testing in the consecutively collected 20.135 CRs from 19.686 servicepersons. As

Read More →

Simulated clinical deployment of fully automatic deep learning for clinical prostate MRI assessment

We use a previously validated artificial neural network to evaluate its performance in a much larger, subsequent, consecutive cohort. In the community, there exists a belief that with infinite training data, an AI system can theoretically be trained that has the ability to handle all possible data and thus be generalised to all environments. Applied to the prostate, this would

Read More →

From community-acquired pneumonia to COVID-19: a deep learning–based method for quantitative analysis of COVID-19 on thick-section CT scans

In this retrospective study, the authors aimed to develop a fully automated artificial intelligence (AI) system to quantitatively assess the severity and progression of COVID-19 using thick-section chest CT images. Through their research and work, they were able to determine that a deep learning-based AI system built on thick-section CT imaging can accurately quantify COVID-19-associated abnormalities in the lung and

Read More →

Automated identification of chest radiographs with referable abnormality with deep learning: need for recalibration

The purpose of this study was to evaluate the calibration of a deep learning (DL) model in a diagnostic cohort, as well as to improve the model’s calibration through recalibration procedures. The authors found that the calibration of the DL algorithm can be augmented through simple recalibration procedures, and improved calibration may enhance the interpretability and credibility of the model

Read More →

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