AIMC Topic: Radiography, Thoracic

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The image quality of deep-learning image reconstruction of chest CT images on a mediastinal window setting.

Clinical radiology Nov 19, 2020
AIM: To assess the image quality of deep-learning image reconstruction (DLIR) of chest computed tomography (CT) images on a mediastinal window setting in comparison to an adaptive statistical iterative reconstruction (ASiR-V).
Middle Aged Deep Learning Radiographic Image Interpretation, Computer-Assisted Radiography, Thoracic Mediastinum Thoracic Diseases Female Aged Male Humans Retrospective Studies Tomography, X-Ray Computed
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Identifying cardiomegaly in chest X-rays: a cross-sectional study of evaluation and comparison between different transfer learning methods.

Acta radiologica (Stockholm, Sweden : 1987) Nov 17, 2020
BACKGROUND: Cardiomegaly is a relatively common incidental finding on chest X-rays; if left untreated, it can result in significant complications. Using Artificial Intelligence for diagnosing cardiomegaly could be beneficial, as this pathology may be...
Datasets as Topic Reference Standards Radiography, Thoracic Algorithms Sensitivity and Specificity Humans Cross-Sectional Studies Artificial Intelligence Machine Learning Feasibility Studies Logistic Models Cardiomegaly Predictive Value of Tests
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Deep learning to predict elevated pulmonary artery pressure in patients with suspected pulmonary hypertension using standard chest X ray.

Scientific reports Nov 17, 2020
Accurate diagnosis of pulmonary hypertension (PH) is crucial to ensure that patients receive timely treatment. We hypothesized that application of artificial intelligence (AI) to the chest X-ray (CXR) could identify elevated pulmonary artery pressure...
Retrospective Studies Female Aged Humans Neural Networks, Computer Algorithms Diagnosis, Computer-Assisted Deep Learning Area Under Curve Heart Failure Aged, 80 and over Male Middle Aged Hypertension, Pulmonary Radiography, Thoracic Pulmonary Artery
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Analyzing inter-reader variability affecting deep ensemble learning for COVID-19 detection in chest radiographs.

PloS one Nov 12, 2020
Data-driven deep learning (DL) methods using convolutional neural networks (CNNs) demonstrate promising performance in natural image computer vision tasks. However, their use in medical computer vision tasks faces several limitations, viz., (i) adapt...
Pneumonia, Viral Observer Variation COVID-19 Radiography, Thoracic Deep Learning Humans Algorithms Betacoronavirus Image Processing, Computer-Assisted SARS-CoV-2 Neural Networks, Computer Coronavirus Infections Pandemics
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Automatic detection of COVID-19 from chest radiographs using deep learning.

Radiography (London, England : 1995) Nov 11, 2020
INTRODUCTION: The breakdown of a deadly infectious disease caused by a newly discovered coronavirus (named SARS n-CoV2) back in December 2019 has shown no respite to slow or stop in general. This contagious disease has spread across different lengths...
Pandemics SARS-CoV-2 Lung Radiography, Thoracic Bronchi Deep Learning Coronavirus Infections Humans
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COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images.

Scientific reports Nov 11, 2020
The Coronavirus Disease 2019 (COVID-19) pandemic continues to have a devastating effect on the health and well-being of the global population. A critical step in the fight against COVID-19 is effective screening of infected patients, with one of the ...
Humans Pandemics COVID-19 Computational Biology Coronavirus Infections Radiography, Thoracic Pneumonia, Viral Tomography, X-Ray Computed Deep Learning Image Processing, Computer-Assisted
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Deep Mining External Imperfect Data for Chest X-Ray Disease Screening.

IEEE transactions on medical imaging Oct 28, 2020
Deep learning approaches have demonstrated remarkable progress in automatic Chest X-ray analysis. The data-driven feature of deep models requires training data to cover a large distribution. Therefore, it is substantial to integrate knowledge from mu...
Thorax X-Rays Deep Learning Radiography Radiography, Thoracic
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Lesion-aware convolutional neural network for chest radiograph classification.

Clinical radiology Oct 16, 2020
AIM: To investigate the performance of a deep-learning approach termed lesion-aware convolutional neural network (LACNN) to identify 14 different thoracic diseases on chest X-rays (CXRs).
Retrospective Studies Female Radiography, Thoracic Reproducibility of Results Male Humans Thoracic Diseases Middle Aged Neural Networks, Computer Radiographic Image Interpretation, Computer-Assisted
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Reproducibility of abnormality detection on chest radiographs using convolutional neural network in paired radiographs obtained within a short-term interval.

Scientific reports Oct 15, 2020
We evaluated the reproducibility of computer-aided detections (CADs) with a convolutional neural network (CNN) on chest radiographs (CXRs) of abnormal pulmonary patterns in patients, acquired within a short-term interval. Anonymized CXRs (n = 9792) o...
Radiography, Thoracic Algorithms Reproducibility of Results Radiographic Image Interpretation, Computer-Assisted Humans Neural Networks, Computer
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Diagnosis of common pulmonary diseases in children by X-ray images and deep learning.

Scientific reports Oct 15, 2020
Acute lower respiratory infection is the leading cause of child death in developing countries. Current strategies to reduce this problem include early detection and appropriate treatment. Better diagnostic and therapeutic strategies are still needed ...
Diagnosis, Computer-Assisted Deep Learning Child Diagnosis, Differential Lung Diseases Humans Radiography, Thoracic Sensitivity and Specificity
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