tomography

Latest posts

Creating a training set for AI from initial segmentations of airways

Ivan Dudurych April 2022

An important challenge in the use of artificial intelligence (AI) for medical image segmentation tasks is the lack of high-quality, scan protocol-specific datasets. AI performs best on narrow tasks with homogenous specifications. Thus, pre-trained models may be inadequate for use in centre-specific studies if the scan protocols do not match. For the airway segmentation task in the Imaging in Lifelines

Machine learning automatically detects COVID-19 using chest CTs in a large multicenter cohort

European Radiology December 2021

This retrospective, multi-institutional study investigated machine learning classifiers and interpretable models using chest CT for the detection of COVID-19 and to differentiate this from types of pneumonia, interstitial lung disease (ILD), and normal CTs. The study included 2,446 chest CTs from across 16 different institutions and the authors’ method was found to accurately differentiate COVID-19 from other types of pneumonia,

A comparison between manual and artificial intelligence–based automatic positioning in CT imaging for COVID-19 patients

European Radiology July 2021

The authors of this study analyzed and compared the imaging workflow, radiation dose, and image quality for 127 adult COVID-19 patients who were examined using either the conventional manual positioning (MP) method or an AI-based automatic positioning (AP) method. The patients underwent chest CT scans using the manual positioning (MP group) for the initial scan, followed by an AI-based automatic

Machine learning based on clinical characteristics and chest CT quantitative measurements for prediction of adverse clinical outcomes in hospitalized patients with COVID-19

European Radiology June 2021

In this study, the authors aimed to develop and validate a machine learning model for the prediction of adverse outcomes in hospitalized patients with COVID-19. They discovered that their findings could be used to facilitate the prediction of adverse outcomes in patients with COVID-19, as well as may allow efficient utilization of medical resources and individualized treatment plans for COVID-19

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

European Radiology November 2020

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

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

European Radiology Experimental October 2020

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

Development and validation of machine learning prediction model based on computed tomography angiography–derived hemodynamics for rupture status of intracranial aneurysms: a Chinese multicenter study

Guo Zhong Chen June 2020

Decisions regarding the optimal management of unruptured intracranial aneurysms (UIAs) depend on a comprehensive comparison of the risks between aneurysm rupture and interventional treatment. The accurate prediction for UIA rupture risk is important for clinicians and patients. Our study further proves that the hemodynamic parameters can improve prediction performance for rupture status of UIAs. Moreover, the AUC of model integrating

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

European Radiology Experimental May 2020

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

Radiomics allows for detection of benign and malignant histopathology in patients with metastatic testicular germ cell tumors prior to post-chemotherapy retroperitoneal Iymph node dissection

Bettina Baeßler April 2020

Among solid tumors, testicular germ cell tumors (TGCT) are the most common entity in men below the age of 40, and they are very likely to metastasize. The so-far unsolved clinical problem for metastasized TGCTs is that as many as 40-50% of patients are overtreated by post-chemotherapy lymph node dissection since the resected lymph nodes show a viable tumor in

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

European Radiology March 2020

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

Machine learning based on clinical characteristics and chest CT quantitative measurements for prediction of adverse clinical outcomes in hospitalized patients with COVID-19

European Radiology June 2021

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

European Radiology November 2020

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

European Radiology Experimental October 2020

Development and validation of machine learning prediction model based on computed tomography angiography–derived hemodynamics for rupture status of intracranial aneurysms: a Chinese multicenter study

Guo Zhong Chen June 2020

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

European Radiology Experimental May 2020

Radiomics allows for detection of benign and malignant histopathology in patients with metastatic testicular germ cell tumors prior to post-chemotherapy retroperitoneal Iymph node dissection

Bettina Baeßler April 2020

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

European Radiology March 2020

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

Latest posts

Creating a training set for AI from initial segmentations of airways

Machine learning automatically detects COVID-19 using chest CTs in a large multicenter cohort

A comparison between manual and artificial intelligence–based automatic positioning in CT imaging for COVID-19 patients

Machine learning based on clinical characteristics and chest CT quantitative measurements for prediction of adverse clinical outcomes in hospitalized patients with COVID-19

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

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

Development and validation of machine learning prediction model based on computed tomography angiography–derived hemodynamics for rupture status of intracranial aneurysms: a Chinese multicenter study

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

Radiomics allows for detection of benign and malignant histopathology in patients with metastatic testicular germ cell tumors prior to post-chemotherapy retroperitoneal Iymph node dissection

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

Creating a training set for AI from initial segmentations of airways

Machine learning automatically detects COVID-19 using chest CTs in a large multicenter cohort

A comparison between manual and artificial intelligence–based automatic positioning in CT imaging for COVID-19 patients

Machine learning based on clinical characteristics and chest CT quantitative measurements for prediction of adverse clinical outcomes in hospitalized patients with COVID-19

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

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

Development and validation of machine learning prediction model based on computed tomography angiography–derived hemodynamics for rupture status of intracranial aneurysms: a Chinese multicenter study

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

Radiomics allows for detection of benign and malignant histopathology in patients with metastatic testicular germ cell tumors prior to post-chemotherapy retroperitoneal Iymph node dissection

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

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tomography

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

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