AIMC Topic: Radiography, Thoracic

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Deep Learning and Binary Relevance Classification of Multiple Diseases using Chest X-Ray images.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Nov 1, 2021
Disease detection using chest X-ray (CXR) images is one of the most popular radiology methods to diagnose diseases through a visual inspection of abnormal symptoms in the lung region. A wide variety of diseases such as pneumonia, heart failure and lu...
Thorax X-Rays Neural Networks, Computer Deep Learning Radiography, Thoracic
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Clinical Artificial Intelligence Applications in Radiology: Chest and Abdomen.

Radiologic clinics of North America Nov 1, 2021
Organ segmentation, chest radiograph classification, and lung and liver nodule detections are some of the popular artificial intelligence (AI) tasks in chest and abdominal radiology due to the wide availability of public datasets. AI algorithms have ...
Humans Gastrointestinal Diseases Radiography, Abdominal Thoracic Diseases Artificial Intelligence Image Interpretation, Computer-Assisted Radiography, Thoracic
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Deep Learning in Chest Radiography: Detection of Pneumoconiosis.

Biomedical and environmental sciences : BES Oct 20, 2021
Humans Deep Learning Diagnosis, Computer-Assisted Pneumoconiosis Radiography, Thoracic
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Deep-Learning-Based Diagnosis of Bedside Chest X-ray in Intensive Care and Emergency Medicine.

Investigative radiology Aug 1, 2021
OBJECTIVES: Validation of deep learning models should separately consider bedside chest radiographs (CXRs) as they are the most challenging to interpret, while at the same time the resulting diagnoses are important for managing critically ill patient...
Critical Care Radiography, Thoracic X-Rays Humans Retrospective Studies Deep Learning Emergency Medicine
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Automated Detection and Quantification of COVID-19 Airspace Disease on Chest Radiographs: A Novel Approach Achieving Expert Radiologist-Level Performance Using a Deep Convolutional Neural Network Trained on Digital Reconstructed Radiographs From Computed Tomography-Derived Ground Truth.

Investigative radiology Aug 1, 2021
OBJECTIVES: The aim of this study was to leverage volumetric quantification of airspace disease (AD) derived from a superior modality (computed tomography [CT]) serving as ground truth, projected onto digitally reconstructed radiographs (DRRs) to (1)...
Retrospective Studies Tomography, X-Ray Computed Male Humans COVID-19 Radiography, Thoracic Cohort Studies Image Processing, Computer-Assisted Lung Radiologists Deep Learning
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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...
Reproducibility of Results Radiologists Quality Improvement Observer Variation Reference Standards Radiography, Thoracic Humans Artificial Intelligence
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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...
Female Sensitivity and Specificity Male Middle Aged Lung Deep Learning Humans Algorithms Radiography, Thoracic Solitary Pulmonary Nodule Burnout, Professional Taiwan Clinical Competence Radiologists Multiple Pulmonary Nodules
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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...
Radiography, Thoracic Humans Image Processing, Computer-Assisted COVID-19 Predictive Value of Tests Retrospective Studies Female Deep Learning
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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...
Pneumothorax Middle Aged Case-Control Studies Observer Variation Deep Learning Radiography, Thoracic Humans Area Under Curve ROC Curve Reproducibility of Results Female Radiographic Image Interpretation, Computer-Assisted Lung Neoplasms Aged Thoracic Neoplasms Lung Diseases Male
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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.
ROC Curve COVID-19 Humans Pneumonia, Viral Neural Networks, Computer Algorithms Diagnosis, Differential Lung Radiography, Thoracic
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