radiography

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

A deep learning algorithm for VHD diagnosis and evaluation

This study aims to develop and validate a deep learning-based automatic chest radiograph (CXR) cardiovascular border (CB) analysis algorithm (CB_auto) in order to diagnose and quantitatively evaluate valvular heart disease (VHD). The authors found that the CB_auto system, in coordination with the deep learning algorithm, provided highly reliable CB measurements, which, in turn, can be useful, not long daily clinical

Read More →

Artificial intelligence in medical imaging practice in Africa: a qualitative content analysis study of radiographers’ perspectives

The aim of this study was to qualitatively explore the perception of radiographers in relation to the integration and acceptance of artificial intelligence (AI) in medical imaging practice on the continent of Africa. Participants of this study consisted solely of radiographers working in Africa between March and August 2020. The study demonstrated a positive outlook regarding AI in relation to

Read More →

COVID-19 classification of X-ray images using deep neural networks

The authors of this retrospective study propose a deep learning model for the detection of COVID-19 from chest x-rays (CXRs), as well as a tool for retrieving similar patients according to the model’s results on their CXRs. The data used for training and evaluating this model was collected from inpatients across four different hospitals. The proposed model achieved accuracy of

Read More →

AI-based improvement in lung cancer detection on chest radiographs: results of a multi-reader study in NLST dataset

Our study included 519 screening chest radiographs (CXRs) from 294 patients enrolled in the National Lung Screening Trial (NLST) who either had proven to have lung cancer or did not have lung cancer over the duration of the trial. Five attending radiologists and three radiology residents from South Korea and the U.S. independently assessed all CXRs for the presence of

Read More →

Initial chest radiographs and artificial intelligence (AI) predict clinical outcomes in COVID-19 patients: analysis of 697 Italian patients

The purpose of this retrospective study was to evaluate whether initial chest X-ray (CXR) severity assessed by an AI system may have prognostic utility in patients with COVID-19. The authors determined, through AI- and radiologist-assessed disease severity scores on CXRs obtained on emergency department (ED) presentation, that they were independent and comparable predictors of adverse outcomes in patients with COVID-19.

Read More →

Practical applications of deep learning: classifying the most common categories of plain radiographs in a PACS using a neural network

The purpose of this study was to classify the most common types of plain radiography through the use of a neural network and, subsequently, to validate the network’s performance on internal and external data. The authors used data from a single institution when classifying the most common categories of radiographs. This study resulted in the authors determining that it is

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 →

Test-retest reproducibility of a deep learning–based automatic detection algorithm for the chest radiograph

The authors of this retrospective study performed test-retest reproducibility analyses for a deep learning-based automatic detection algorithm (DLAD) using two stationary chest radiographs with short-term intervals, in order to analyze influential factors on test-retest variations. The test, which included patients with pulmonary nodules resected in 2017, showed that DLAD was robust to the test-retest variation. Key points The deep learning–based

Read More →

What the increasing presence of AI means for radiographers

In an age of uncertainty with the arrival of artificial intelligence (AI) tools and technologies in the healthcare field, many in the industry question how the addition of AI will impact their careers. One particular area is not immune to these changes: radiography. We spoke with Dr. Nick Woznitza, a reporting radiographer at Homerton University Hospital and a clinical academic

Read More →

A deep learning algorithm for VHD diagnosis and evaluation

This study aims to develop and validate a deep learning-based automatic chest radiograph (CXR) cardiovascular border (CB) analysis algorithm (CB_auto) in order to diagnose and quantitatively evaluate valvular heart disease (VHD). The authors found that the CB_auto system, in coordination with the deep learning algorithm, provided highly reliable CB measurements, which, in turn, can be useful, not long daily clinical

Read More →

Artificial intelligence in medical imaging practice in Africa: a qualitative content analysis study of radiographers’ perspectives

The aim of this study was to qualitatively explore the perception of radiographers in relation to the integration and acceptance of artificial intelligence (AI) in medical imaging practice on the continent of Africa. Participants of this study consisted solely of radiographers working in Africa between March and August 2020. The study demonstrated a positive outlook regarding AI in relation to

Read More →

COVID-19 classification of X-ray images using deep neural networks

The authors of this retrospective study propose a deep learning model for the detection of COVID-19 from chest x-rays (CXRs), as well as a tool for retrieving similar patients according to the model’s results on their CXRs. The data used for training and evaluating this model was collected from inpatients across four different hospitals. The proposed model achieved accuracy of

Read More →

AI-based improvement in lung cancer detection on chest radiographs: results of a multi-reader study in NLST dataset

Our study included 519 screening chest radiographs (CXRs) from 294 patients enrolled in the National Lung Screening Trial (NLST) who either had proven to have lung cancer or did not have lung cancer over the duration of the trial. Five attending radiologists and three radiology residents from South Korea and the U.S. independently assessed all CXRs for the presence of

Read More →

Initial chest radiographs and artificial intelligence (AI) predict clinical outcomes in COVID-19 patients: analysis of 697 Italian patients

The purpose of this retrospective study was to evaluate whether initial chest X-ray (CXR) severity assessed by an AI system may have prognostic utility in patients with COVID-19. The authors determined, through AI- and radiologist-assessed disease severity scores on CXRs obtained on emergency department (ED) presentation, that they were independent and comparable predictors of adverse outcomes in patients with COVID-19.

Read More →

Practical applications of deep learning: classifying the most common categories of plain radiographs in a PACS using a neural network

The purpose of this study was to classify the most common types of plain radiography through the use of a neural network and, subsequently, to validate the network’s performance on internal and external data. The authors used data from a single institution when classifying the most common categories of radiographs. This study resulted in the authors determining that it is

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 →

Test-retest reproducibility of a deep learning–based automatic detection algorithm for the chest radiograph

The authors of this retrospective study performed test-retest reproducibility analyses for a deep learning-based automatic detection algorithm (DLAD) using two stationary chest radiographs with short-term intervals, in order to analyze influential factors on test-retest variations. The test, which included patients with pulmonary nodules resected in 2017, showed that DLAD was robust to the test-retest variation. Key points The deep learning–based

Read More →

What the increasing presence of AI means for radiographers

In an age of uncertainty with the arrival of artificial intelligence (AI) tools and technologies in the healthcare field, many in the industry question how the addition of AI will impact their careers. One particular area is not immune to these changes: radiography. We spoke with Dr. Nick Woznitza, a reporting radiographer at Homerton University Hospital and a clinical academic

Read More →

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  • Reduced registration fees for ECR 1
  • Free electronic access to the journal European Radiology
  • Content e-mails for all 3 ESR journals 4
  • Updates on offers & events through our newsletters
  • Exclusive access to the ESR feed in Juisci

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Footnotes:

01

Reduced registration fees for ECR 2024:
Provided that ESR 2023 membership is activated and approved by August 31, 2023.

Reduced registration fees for ECR 2025:
Provided that ESR 2024 membership is activated and approved by August 31, 2024.

02
Not all activities included
03
Examination based on the ESR European Training Curriculum (radiologists or radiology residents).
04
European Radiology, Insights into Imaging, European Radiology Experimental.