AIMC Topic: Radiography, Thoracic

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Improving reference standards for validation of AI-based radiography.

The British journal of radiology Jul 1, 2021
OBJECTIVE: Demonstrate the importance of combining multiple readers' opinions, in a context-aware manner, when establishing the reference standard for validation of artificial intelligence (AI) applications for, chest radiographs. By comparing indiv...
Quality Improvement Observer Variation Reference Standards Radiography, Thoracic Humans Artificial Intelligence Reproducibility of Results Radiologists
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Performance and educational training of radiographers in lung nodule or mass detection: Retrospective comparison with different deep learning algorithms.

Medicine Jun 11, 2021
The aim of this investigation was to compare the diagnostic performance of radiographers and deep learning algorithms in pulmonary nodule/mass detection on chest radiograph.A test set of 100 chest radiographs containing 53 cases with no pathology (no...
Deep Learning Burnout, Professional Clinical Competence Multiple Pulmonary Nodules Solitary Pulmonary Nodule Taiwan Radiologists Radiography, Thoracic Sensitivity and Specificity Humans Male Middle Aged Algorithms Lung Female
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A Deep-Learning Diagnostic Support System for the Detection of COVID-19 Using Chest Radiographs: A Multireader Validation Study.

Investigative radiology Jun 1, 2021
MATERIALS AND METHODS: Five publicly available databases comprising normal CXR, confirmed COVID-19 pneumonia cases, and other pneumonias were used. After the harmonization of the data, the training set included 7966 normal cases, 5451 with other pneu...
Deep Learning Image Processing, Computer-Assisted Predictive Value of Tests Radiography, Thoracic Humans COVID-19 Retrospective Studies Female
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Evaluation of a deep learning-based computer-aided detection algorithm on chest radiographs: Case-control study.

Medicine Apr 23, 2021
Along with recent developments in deep learning techniques, computer-aided diagnosis (CAD) has been growing rapidly in the medical imaging field. In this work, we evaluate the deep learning-based CAD algorithm (DCAD) for detecting and localizing 3 ma...
Middle Aged Case-Control Studies Deep Learning ROC Curve Reproducibility of Results Aged Male Lung Diseases Observer Variation Radiography, Thoracic Pneumothorax Lung Neoplasms Thoracic Neoplasms Area Under Curve Radiographic Image Interpretation, Computer-Assisted Humans Female
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FLANNEL (Focal Loss bAsed Neural Network EnsembLe) for COVID-19 detection.

Journal of the American Medical Informatics Association : JAMIA Mar 1, 2021
OBJECTIVE: The study sought to test the possibility of differentiating chest x-ray images of coronavirus disease 2019 (COVID-19) against other pneumonia and healthy patients using deep neural networks.
Algorithms Lung ROC Curve Diagnosis, Differential Radiography, Thoracic Pneumonia, Viral Humans Neural Networks, Computer COVID-19
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A machine-learning based approach to quantify fine crackles in the diagnosis of interstitial pneumonia: A proof-of-concept study.

Medicine Feb 19, 2021
Fine crackles are frequently heard in patients with interstitial lung diseases (ILDs) and are known as the sensitive indicator for ILDs, although the objective method for analyzing respiratory sounds including fine crackles is not clinically availabl...
Respiratory Sounds Lung Diseases, Interstitial Radiography, Thoracic Auscultation Female Aged Aged, 80 and over Male Middle Aged Disease Progression Lung Idiopathic Pulmonary Fibrosis Humans Machine Learning Algorithms Tomography, X-Ray Computed
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Integrity of clinical information in radiology reports documenting pulmonary nodules.

Journal of the American Medical Informatics Association : JAMIA Jan 15, 2021
OBJECTIVE: Quantify the integrity, measured as completeness and concordance with a thoracic radiologist, of documenting pulmonary nodule characteristics in CT reports and assess impact on making follow-up recommendations.
Radiography, Thoracic Retrospective Studies Solitary Pulmonary Nodule Tomography, X-Ray Computed Aged Humans Natural Language Processing Electronic Health Records Male Documentation Female Middle Aged Radiology Information Systems Multiple Pulmonary Nodules
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COVID-19 diagnosis from chest X-ray images using transfer learning: Enhanced performance by debiasing dataloader.

Journal of X-ray science and technology Jan 1, 2021
BACKGROUND: Chest X-ray imaging has been proved as a powerful diagnostic method to detect and diagnose COVID-19 cases due to its easy accessibility, lower cost and rapid imaging time.
Deep Learning Algorithms Humans Pneumonia Tomography, X-Ray Computed Neural Networks, Computer Early Diagnosis Reproducibility of Results COVID-19 Radiography, Thoracic SARS-CoV-2
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Deep Transfer Learning for COVID-19 Prediction: Case Study for Limited Data Problems.

Current medical imaging Jan 1, 2021
OBJECTIVE: Automatic prediction of COVID-19 using deep convolution neural networks based pre-trained transfer models and Chest X-ray images.
Deep Learning Radiography, Thoracic Humans Neural Networks, Computer COVID-19 SARS-CoV-2
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Determination of disease severity in COVID-19 patients using deep learning in chest X-ray images.

Diagnostic and interventional radiology (Ankara, Turkey) Jan 1, 2021
PURPOSE: Chest X-ray plays a key role in diagnosis and management of COVID-19 patients and imaging features associated with clinical elements may assist with the development or validation of automated image analysis tools. We aimed to identify associ...
Adult Radiologists Male Feasibility Studies Middle Aged Severity of Illness Index Retrospective Studies Radiography, Thoracic Comorbidity Thorax Deep Learning Female SARS-CoV-2 Age Factors Humans Aged Italy COVID-19
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