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Back to main AI Blog

lung

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.

Most used hashtags:

deep learningmachine learningmriradiomicsx-ray computed tomographyinterviewai newsmammographyCOVID-19diagnostic imagingneural networksbreast cancerworkflowbreast neoplasmstomographyradiography

Latest posts

External validation, radiological evaluation, and development of DL lung segmentation in chest CT

European Radiology January 2024

The authors of this study developed a 3D nnU-Net-based model for automatic lung segmentation in computed tomography pulmonary angiography (CTPA) imaging that was found to be highly accurate, clinically evaluated, and externally tested in patient cohorts with a spread of lung disease. Key points Accurate, externally validated CT pulmonary angiography (CTPA) lung segmentation model tested in two large heterogeneous clinical

Read More →

Evaluating a deep learning software for lung parenchyma characterization in COVID-19 pneumonia

European Radiology Experimental August 2023

The aim of this study was to evaluate the performance of the LungQuant system, which is a deep learning-based software for quantitative analysis of chest CT. LungQuant was evaluated by comparing its results with independent visual evaluations by a group of clinical experts. The results indicated that an automatic quantification tool may be beneficial and contribute to an improved clinical

Read More →

Radiomic nomogram predicts pathology invasiveness

European Radiology March 2022

The purpose of this retrospective diagnostic study was to develop and validate a preoperative CT-based nomogram combined with radiomic and clinical-radiological signatures to distinguish preinvasive lesions from pulmonary invasive lesions. The authors were able to demonstrate that a nomogram constructed by identified clinical-radiological signatures and combined radiomic signatures has the potential to precisely predict pathology invasiveness. Key points The radiomic

Read More →

CT-based radiomics and machine learning to predict spread through air space in lung adenocarcinoma

European Radiology October 2020

The authors of this retrospective study aimed to develop and validate a CT-based radiomics model for preoperative prediction of spread through air space (STAS) in lung adenocarcinoma. They found that a CT-based radiomics model can preoperatively predict, with good diagnosis performance, STAS in lung adenocarcinoma. Key points CT-based radiomics and machine learning model can predict spread through air space (STAS)

Read More →

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

European Radiology June 2020

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: definition and perspectives for thoracic imaging

European Radiology May 2020

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 →

Prediction of pulmonary pressure after Glenn shunts by computed tomography–based machine learning models

European Radiology January 2020

The aim of this retrospective study was to develop non-invasive machine learning (ML) classifiers for predicting post-Glenn shunt patients with low and high risks of a mean pulmonary arterial pressure above 15 mmHg, which was based on pre-operative cardiac CT. The study included 96 patients who underwent a bidirectional Glenn procedure. Key points Twenty-three candidate descriptors were manually extracted from

Read More →

External validation, radiological evaluation, and development of DL lung segmentation in chest CT

European Radiology January 2024

The authors of this study developed a 3D nnU-Net-based model for automatic lung segmentation in computed tomography pulmonary angiography (CTPA) imaging that was found to be highly accurate, clinically evaluated, and externally tested in patient cohorts with a spread of lung disease. Key points Accurate, externally validated CT pulmonary angiography (CTPA) lung segmentation model tested in two large heterogeneous clinical

Read More →

Evaluating a deep learning software for lung parenchyma characterization in COVID-19 pneumonia

European Radiology Experimental August 2023

The aim of this study was to evaluate the performance of the LungQuant system, which is a deep learning-based software for quantitative analysis of chest CT. LungQuant was evaluated by comparing its results with independent visual evaluations by a group of clinical experts. The results indicated that an automatic quantification tool may be beneficial and contribute to an improved clinical

Read More →

Radiomic nomogram predicts pathology invasiveness

European Radiology March 2022

The purpose of this retrospective diagnostic study was to develop and validate a preoperative CT-based nomogram combined with radiomic and clinical-radiological signatures to distinguish preinvasive lesions from pulmonary invasive lesions. The authors were able to demonstrate that a nomogram constructed by identified clinical-radiological signatures and combined radiomic signatures has the potential to precisely predict pathology invasiveness. Key points The radiomic

Read More →

CT-based radiomics and machine learning to predict spread through air space in lung adenocarcinoma

European Radiology October 2020

The authors of this retrospective study aimed to develop and validate a CT-based radiomics model for preoperative prediction of spread through air space (STAS) in lung adenocarcinoma. They found that a CT-based radiomics model can preoperatively predict, with good diagnosis performance, STAS in lung adenocarcinoma. Key points CT-based radiomics and machine learning model can predict spread through air space (STAS)

Read More →

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

European Radiology June 2020

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: definition and perspectives for thoracic imaging

European Radiology May 2020

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 →

Prediction of pulmonary pressure after Glenn shunts by computed tomography–based machine learning models

European Radiology January 2020

The aim of this retrospective study was to develop non-invasive machine learning (ML) classifiers for predicting post-Glenn shunt patients with low and high risks of a mean pulmonary arterial pressure above 15 mmHg, which was based on pre-operative cardiac CT. The study included 96 patients who underwent a bidirectional Glenn procedure. Key points Twenty-three candidate descriptors were manually extracted from

Read More →

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You will have access to a wide range of benefits that can help you advance your career and stay up-to-date with the latest developments in the field of radiology. These benefits include access to educational resources, networking opportunities with other professionals in the field, opportunities to participate in research projects and clinical trials, and access to the latest technologies and techniques. 

Check out our different membership options.

If you don’t find a fitting membership send us an email here.

①

Membership

for radiologists, radiology residents, professionals of allied sciences (including radiographers/radiological technologists, nuclear medicine physicians, medical physicists, and data scientists) & professionals of allied sciences in training residing within the boundaries of Europe

  • Reduced registration fees for ECR 1
  • Reduced fees for the European School of Radiology (ESOR) 2
  • Option to participate in the European Diploma. 3
  • Free electronic access to the journal European Radiology 
  • Content e-mails for all ESR journals4
  • Updates on offers & events through our newsletters
  • Exclusive access to the ESR feed in Juisci

€ 11 /year

Yes! That is less than €1 per month.
②

Free membership

for radiologists, radiology residents or professionals of allied sciences engaged in practice, teaching or research residing outside Europe as well as individual qualified professionals with an interest in radiology and medical imaging who do not fulfil individual or all requirements for any other ESR membership category & former full members who have retired from all clinical practice
  • Reduced registration fees for ECR 1
  • Option to participate in the European Diploma. 3
  • Free electronic access to the journal European Radiology
  • Content e-mails for all ESR journals 4
  • Updates on offers & events through our newsletters
  • Exclusive access to the ESR feed in Juisci

€ 0

The best things in life are free.
③

ESR Friends

For students, company representatives or hospital managers etc.

  • Content e-mails for all 3 ESR journals 4
  • Updates on offers & events through our newsletters

€ 0

Friendship doesn’t cost a thing.
Apply now

The membership type best fitting for you will be selected automatically during the application process.

Footnotes:

01

Reduced registration fees for ECR 2027:
Provided that ESR 2026 membership is activated and approved by August 31, 2026.

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.