thorax

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

Reproducibility of a combined AI and optimal-surface graph-cut method to automate bronchial parameter extraction

The authors of this study evaluated the reproducibility of a deep learning and optimal-surface graph-cut method to automatically segment the airway lumen and wall, and calculate bronchial parameters. A deep-learning model was trained on 24 low-dose chest CT scans. The study demonstrated a comprehensive and fully automatic pipeline for bronchial parameter measurement on low-dose CT using open-source tools. Key points

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Creating a training set for AI from initial segmentations of airways

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

Read More →

A fully automatic artificial intelligence–based CT image analysis system for accurate detection, diagnosis, and quantitative severity evaluation of pulmonary tuberculosis

The authors of this study aimed to develop an artificial intelligence (AI)-based fully automated CT image analysis system in order to detect and diagnose pulmonary tuberculosis (TB). This was achieved through the retrospective use of 892 chest CT scans from pathogen-confirmed TB patients. It was found that the end-to-end AI system based on chest CT is able to achieve human-level

Read More →

Radiomics approach for survival prediction in chronic obstructive pulmonary disease

The idea of quantification of disease severity of chronic obstructive pulmonary disease (COPD) with CT has been introduced as early as the late 1980s with the so-called ‘density mask’ method for emphysema quantification. Since then, many novel methods of quantification, including the assessment of airway wall thickening, air trapping, vascular change and so on, have been introduced, and many studies

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 →

Deep learning: definition and perspectives for thoracic imaging

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 →

Reproducibility of a combined AI and optimal-surface graph-cut method to automate bronchial parameter extraction

The authors of this study evaluated the reproducibility of a deep learning and optimal-surface graph-cut method to automatically segment the airway lumen and wall, and calculate bronchial parameters. A deep-learning model was trained on 24 low-dose chest CT scans. The study demonstrated a comprehensive and fully automatic pipeline for bronchial parameter measurement on low-dose CT using open-source tools. Key points

Read More →

Creating a training set for AI from initial segmentations of airways

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

Read More →

A fully automatic artificial intelligence–based CT image analysis system for accurate detection, diagnosis, and quantitative severity evaluation of pulmonary tuberculosis

The authors of this study aimed to develop an artificial intelligence (AI)-based fully automated CT image analysis system in order to detect and diagnose pulmonary tuberculosis (TB). This was achieved through the retrospective use of 892 chest CT scans from pathogen-confirmed TB patients. It was found that the end-to-end AI system based on chest CT is able to achieve human-level

Read More →

Radiomics approach for survival prediction in chronic obstructive pulmonary disease

The idea of quantification of disease severity of chronic obstructive pulmonary disease (COPD) with CT has been introduced as early as the late 1980s with the so-called ‘density mask’ method for emphysema quantification. Since then, many novel methods of quantification, including the assessment of airway wall thickening, air trapping, vascular change and so on, have been introduced, and many studies

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 →

Deep learning: definition and perspectives for thoracic imaging

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 →

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