thoracic 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

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

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Artificial intelligence and radiomics enhance the positive predictive value of digital chest tomosynthesis for lung cancer detection within SOS clinical trial

Digital tomosynthesis (DTS) could be a realistic alternative to low dose CT for lung cancer screening, in particular for detecting nodules larger than 5 mm. In the past years, within SOS clinical trial we found that the probability of detecting lung cancer with the DTS in a population of heavy smokers older than 45 years was around 1%, a value

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

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

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Deep learning-based detection system for multiclass lesions on chest radiographs: comparison with observer readings

In this study, the authors sought to investigate the feasibility of a deep learning-based detection (DLD) system for multiclass legions on chest radiograph. The study included almost 16,000 chest radiographs collected from two tertiary hospitals, which were then used to develop a DLD system. The authors found that the DLD system showed potential for detections of lesions and pattern classification.

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 and radiomics enhance the positive predictive value of digital chest tomosynthesis for lung cancer detection within SOS clinical trial

Digital tomosynthesis (DTS) could be a realistic alternative to low dose CT for lung cancer screening, in particular for detecting nodules larger than 5 mm. In the past years, within SOS clinical trial we found that the probability of detecting lung cancer with the DTS in a population of heavy smokers older than 45 years was around 1%, a value

Read More →

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

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-based detection system for multiclass lesions on chest radiographs: comparison with observer readings

In this study, the authors sought to investigate the feasibility of a deep learning-based detection (DLD) system for multiclass legions on chest radiograph. The study included almost 16,000 chest radiographs collected from two tertiary hospitals, which were then used to develop a DLD system. The authors found that the DLD system showed potential for detections of lesions and pattern classification.

Read More →

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  • Option to participate in the European Diploma. 3
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  • Updates on offers & events through our newsletters
  • Exclusive access to the ESR feed in Juisci

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Footnotes:

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Reduced registration fees for ECR 2025:
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