AIMC Topic: Pulmonary Emphysema
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Pulmonary emphysema quantification at low dose chest CT using Deep Learning image reconstruction.
European journal of radiology
May 5, 2022
PURPOSE: Quantitative analysis of emphysema volume is affected by the radiation dose and the CT reconstruction technique. We aim to evaluate the influence of a commercially available deep learning image reconstruction algorithm (DLIR) on the quantifi...
A Challenge for Emphysema Quantification Using a Deep Learning Algorithm With Low-dose Chest Computed Tomography.
Journal of thoracic imaging
Apr 5, 2022
PURPOSE: We aimed to identify clinically relevant deep learning algorithms for emphysema quantification using low-dose chest computed tomography (LDCT) through an invitation-based competition.
COPD identification and grading based on deep learning of lung parenchyma and bronchial wall in chest CT images.
The British journal of radiology
Feb 16, 2022
OBJECTIVE: Chest CT can display the main pathogenic factors of chronic obstructive pulmonary disease (COPD), emphysema and airway wall remodeling. This study aims to establish deep convolutional neural network (CNN) models using these two imaging mar...
The Normal Lung Index From Quantitative Computed Tomography for the Evaluation of Obstructive and Restrictive Lung Disease.
Journal of thoracic imaging
Dec 16, 2021
PURPOSE: Our objective was to evaluate whether the normal lung index (NLI) from quantitative computed tomography (QCT) analysis can be used to predict mortality as well as pulmonary function tests (PFTs) in patients with chronic obstructive pulmonary...
Improving clinical disease subtyping and future events prediction through a chest CT-based deep learning approach.
Medical physics
Jan 27, 2021
PURPOSE: To develop and evaluate a deep learning (DL) approach to extract rich information from high-resolution computed tomography (HRCT) of patients with chronic obstructive pulmonary disease (COPD).
Deep neural network analyses of spirometry for structural phenotyping of chronic obstructive pulmonary disease.
JCI insight
Jul 9, 2020
BACKGROUNDCurrently recommended traditional spirometry outputs do not reflect the relative contributions of emphysema and airway disease to airflow obstruction. We hypothesized that machine-learning algorithms can be trained on spirometry data to ide...
Emphysema quantification using low-dose computed tomography with deep learning-based kernel conversion comparison.
European radiology
Jun 29, 2020
OBJECTIVE: This study determined the effect of dose reduction and kernel selection on quantifying emphysema using low-dose computed tomography (LDCT) and evaluated the efficiency of a deep learning-based kernel conversion technique in normalizing ker...
Comparison of Artificial Intelligence-Based Fully Automatic Chest CT Emphysema Quantification to Pulmonary Function Testing.
AJR. American journal of roentgenology
Mar 4, 2020
The purpose of this study was to evaluate an artificial intelligence (AI)-based prototype algorithm for fully automated quantification of emphysema on chest CT compared with pulmonary function testing (spirometry). A total of 141 patients (72 women...
Learning to Quantify Emphysema Extent: What Labels Do We Need?
IEEE journal of biomedical and health informatics
Aug 1, 2019
Accurate assessment of pulmonary emphysema is crucial to assess disease severity and subtype, to monitor disease progression, and to predict lung cancer risk. However, visual assessment is time-consuming and subject to substantial inter-rater variabi...
A convolutional neural network for ultra-low-dose CT denoising and emphysema screening.
Medical physics
Jul 17, 2019
PURPOSE: Reducing dose level to achieve ALARA is an important task in diagnostic and therapeutic applications of computed tomography (CT) imaging. Effective image quality enhancement strategies are crucial to compensate for the degradation caused by ...
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