deep learning

Latest posts

Fully automated pelvic bone segmentation in multiparametric MRI using a 3D convolutional neural network

Xiaoying Wang August 2021

The accurate skeleton segmentation with their semantic labels represents the initial step to achieve accurate prostate cancer bone metastases detection on DWI and ADC images. Several studies have reported convolutional neural networks (CNNs) for the segmentation of normal bone structures on CT images and bone scans; however, only a few studies on automatic segmentation of normal bone structures on MR

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

European Radiology August 2021

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

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

So Yeon Kim July 2021

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

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

European Radiology May 2021

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

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

European Radiology May 2021

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,

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

Masahiro Yanagawa May 2021

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

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

European Radiology April 2021

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

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

Chang Min Park February 2021

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

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

David Bonekamp January 2021

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

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

European Radiology December 2020

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

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

European Radiology May 2021

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

Masahiro Yanagawa May 2021

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

European Radiology April 2021

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

Chang Min Park February 2021

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

David Bonekamp January 2021

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

European Radiology December 2020

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

Latest posts

Fully automated pelvic bone segmentation in multiparametric MRI using a 3D convolutional neural network

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

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

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

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

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

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

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

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

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

Fully automated pelvic bone segmentation in multiparametric MRI using a 3D convolutional neural network

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

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

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

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

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

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

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

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

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

Check out our different membership options.

Membership

Free membership

ESR Friends

Footnotes:

deep learning

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

Latest posts

Become A Member Today!

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