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14 results for “Chest radiography”
Supporting data: phase-contrast virtual chest radiography
<p>This dataset contains supporting data for the publication below:</p> <ul> <li>Ilian Häggmark, Kian Shaker, Sven Nyrén, Bariq Al-Amiry, Ehsan Abadi, William P. Segars, Ehsan Samei, and Hans M. Hertz, "Phase-contrast virtual chest radiography<em>"</em>, <em>Proceedings of the National Academy of Sciences </em><strong>120</strong><em> </em>(1), e2210214120 (2023). <a href="https://doi.org/10.1073/pnas.2210214120">https://doi.org/10.1073/pnas.2210214120</a></li> </ul> <p>If you use this dataset for your work, <strong>please cite this publication.</strong></p> <p>-------------------------------------</p> <p><strong>1. Virtual patient (2D) </strong></p> <p>An upsampled and projected virtual patient derived from the XCAT model (see paper for more details). The projected thickness (unit: [m]) of 28 separate materials are stored in the mat-file <em>'virtual_patient.mat'</em>. The accompanying text file <em>'virtual_patient_materials.txt' </em>lists all 28 materials (3rd dimension in the virtual patient .mat file) .</p> <p>Data information:</p> <ul> <li>File type: .mat</li> <li>Size: 39200x52200x28 (3D matrix, single, 32-bit)</li> <li>Pixel size: 7.69x7.69 µm<sup>2</sup></li> </ul> <p>-------------------------------------</p> <p><strong>2. Full-chest virtual radiographs</strong></p> <p>Three full-size virtual chest radiographs of the virtual patient (above) simulated with different settings:</p> <ul> <li>Conventional (<em>z</em> = 0 m, 120 kVp tungsten spectrum)</li> <li>Control (<em>z</em> = 0 m, 60 keV monochromatic)</li> <li>Phase contrast (<em>z</em> = 12 m, 60 keV monochromatic)</li> </ul> <p>Data information:</p> <ul> <li>File type: .tif (16-bit)</li> <li>Data size: 8000x6000 pixels</li> <li>Pixel size: 50x50 µm<sup>2</sup></li> </ul> <p>-------------------------------------</p> <p><strong>3. Zoom in on chest radiographs at different propagation distances</strong></p> <p>This is the underlying data for <strong>Figure 2</strong> in the paper.</p> <p>Data information:</p> <ul> <li>File type: .tif (16-bit)</li> <li>Data size: 600x600 pixels</li> <li>Pixel size: 50x50 µm<sup>2</sup></li> </ul> <p>-------------------------------------</p> <p><strong>4. Observing airway wall thickening</strong></p> <p>This is the underlying data for<strong> Figure 5</strong> in the paper.</p> <p>Data information:</p> <ul> <li>File type: .tif (16-bit)</li> <li>Data size: 750x750 pixels</li> <li>Pixel size: 50x50 µm<sup>2</sup></li> </ul>
Measurement of Lung Area at Chest Radiography to Define the Prognosis in Newborns With CDH
ClinicalTrials.gov study NCT04396028. IPD Sharing: Not stated. Countries: 1. Publications: 22.
Lung Ultrasound Versus Chest Radiography for Detection of Pneumothorax
ClinicalTrials.gov study NCT06022081. IPD Sharing: YES. Countries: 1. Publications: 10.
Point of Care Echocardiography Versus Chest Radiography for the Assessment of Central Venous Catheter Placement
ClinicalTrials.gov study NCT02661607. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Comparison Between Lung Ultrasound and Chest Radiography for Acute Dyspnea
ClinicalTrials.gov study NCT02105207. IPD Sharing: Not stated. Countries: 1. Publications: 28.
Clinical applicability of COVID-19 detection in chest radiography with deep learning - radiologist annotations
<p>Chest radiography (CXR) can be used as a complementary method for diagnosing/following COVID-19 patients. However, experience level and workload of technicians and radiologists may affect the decision process. In order to evaluate the performance of radiologists in the detection of COVID-19 radiological features in CXR, manual annotation of a subset of random CXR images was performed by two radiologists using an in-house software. The software presented CXRs from a randomly selected subset and allowed for window center/width adjustment, zooming and panning. Radiologists were asked to label CXRs into one of 4 classes: Normal, Not indicative of COVID-19 (pathological), Indicative of COVID-19 and Undetermined. The Indicative of COVID-19 class was defined as CXRs where the patient presented findings indicative of COVID-19, namely bilateral pulmonary opacities of low/medium density. The Undetermined class was defined as CXRs where the patient presented findings that could be indicative of COVID-19 but which could also be indicative of another condition, namely unilateral lung opacities, diffuse bilateral opacities of ARDS pattern or diffuse reticular opacities. The Not indicative of COVID-19 (pathological) class was defined as CXRs where the patient presented findings indicative of any other pathology except for COVID-19. CXRs where the patient presented medical devices were classified as Normal if the underlying pathology was not visible. Additionally, CXRs without sufficient quality for visual assessment by the radiologists due to bad image quality, patient positioning or any other factors could be labelled as Compromised for exclusion. A total of 1,845 CXRs were selected for annotation by two radiologists. Of these, 1,256 belong to the Mixed dataset(a combination of multiple public sources), 289 belong to BIMCV and 300 belong to COVIDGR. Selection of CXRs for annotation was performed randomly: for the Mixed dataset, a balanced selection strategy was used during image selection, whereas for BIMCV and COVIDGR, the dataset class distribution was maintained in the subset selected for annotation. Manual labelling of CXRs was performed in two stages. First, both radiologists independently classified each CXR. CXRs where the two radiologists disagreed were then selected for the second stage where the two radiologists assessed the CXRs together to achieve consensus. At no point were radiologists given access to the ground truth label, RT-PCR results or any other information besides the CXR image. To ensure that written information present in the CXR image (such as hospital system, health service, laterality markers, patient positioning, etc.) did not bias the annotation, all written labels were blacked out during before annotation. This dataset contains the information regarding each annotated CXR and the resulting radiologist labels for each image before and after consensus.</p> <p>The public datasets used in this study are available in the following repositories:</p> <p>CheXpert: <a href="https://stanfordmlgroup.github.io/competitions/chexpert/">https://stanfordmlgroup.github.io/competitions/chexpert/</a></p> <p>ChestXRay-8: <a href="https://nihcc.app.box.com/v/ChestXray-NIHCC">https://nihcc.app.box.com/v/ChestXray-NIHCC</a></p> <p>COVID-19 IDC: <a href="https://github.com/ieee8023/covid-chestxray-dataset">https://github.com/ieee8023/covid-chestxray-dataset</a></p> <p>COVIDx: <a href="https://github.com/lindawangg/COVID-Net">https://github.com/lindawangg/COVID-Net</a></p> <p>RSNA-PDC: <a href="https://www.kaggle.com/c/rsna-pneumonia-detection-challenge">https://www.kaggle.com/c/rsna-pneumonia-detection-challenge</a></p> <p>SAVE LIVES: <a href="https://www.hmhospitales.com/coronavirus/covid-data-save-lives">https://www.hmhospitales.com/coronavirus/covid-data-save-lives</a></p> <p>SERAM: <a href="https://seram.es/images/site/TUTORIAL_CSI_RX_TORAX_COVID-19_vs_4.0.pdf">https://seram.es/images/site/TUTORIAL_CSI_RX_TORAX_COVID-19_vs_4.0.pdf</a></p> <p>Twitter: <a href="https://twitter.com/ChestImaging">https://twitter.com/ChestImaging</a></p> <p>BIMCV PADCHEST: <a href="https://bimcv.cipf.es/bimcv-projects/padchest/">https://bimcv.cipf.es/bimcv-projects/padchest/</a></p> <p>BIMCV COVID-19-PADCHEST: <a href="https://bimcv.cipf.es/bimcv-projects/bimcv-covid19/">https://bimcv.cipf.es/bimcv-projects/bimcv-covid19/</a></p> <p>COVIDGR: <a href="https://dasci.es/transferencia/open-data/covidgr-2/">https://dasci.es/transferencia/open-data/covidgr-2/</a></p>
Comparison of Lung Ultrasound and Chest Radiography for Pleural and Pulmonary Diagnosis in Pediatric Intensive Care Unit
ClinicalTrials.gov study NCT03939728. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Screening for Pneumonia: A Comparison of Ultra Low Dose Chest CT [ULD-CT] and Conventional Chest Radiography [CXR]
ClinicalTrials.gov study NCT03140163. IPD Sharing: NO. Countries: 1. Publications: 0.
Diaphragm Dysfunction After COVID-19 as Determined by Dynamic Chest Radiography
ClinicalTrials.gov study NCT06175455. IPD Sharing: NO. Countries: 1. Publications: 0.
Chest Radiography in COVID-19 Patients
ClinicalTrials.gov study NCT04485338. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Exploring Gas Transfer and the Utility of Dynamic Chest Radiography in Long Covid Patients
ClinicalTrials.gov study NCT05862896. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Trial of Artificial Intelligence for Chest Radiography
ClinicalTrials.gov study NCT06456203. IPD Sharing: NO. Countries: 0. Publications: 0.
Nurse-performed Lung Ultrasound Versus Chest Radiography for Detection of Pneumothorax.
ClinicalTrials.gov study NCT04678726. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Potential of Deep Learning in Assessing Pneumoconiosis Depicted on Digital Chest Radiography
ClinicalTrials.gov study NCT04963348. IPD Sharing: NO. Countries: 0. Publications: 0.
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