pneumothorax

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

Pneumothorax detection in chest radiographs: optimizing artificial intelligence system for accuracy and confounding bias reduction using in-image annotations in algorithm training

Due to the noisy annotation quality of public training data and confounding thoracic tubes, the diagnostic accuracy of artificial intelligence (AI) pneumothorax (PTX) detection in chest radiographs is limited. Therefore, the authors of this study hypothesized that in-image annotations of the dehiscent visceral pleura for algorithm training boosts the algorithm’s performance and suppresses confounders. Their results in this study are

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

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

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Pneumothorax detection in chest radiographs: optimizing artificial intelligence system for accuracy and confounding bias reduction using in-image annotations in algorithm training

Due to the noisy annotation quality of public training data and confounding thoracic tubes, the diagnostic accuracy of artificial intelligence (AI) pneumothorax (PTX) detection in chest radiographs is limited. Therefore, the authors of this study hypothesized that in-image annotations of the dehiscent visceral pleura for algorithm training boosts the algorithm’s performance and suppresses confounders. Their results in this study are

Read More →

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

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

Read More →

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