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77 results for “radiography”
The Efficacy of Cone Beam Computed Tomography (CBCT) Compared to Panoramic Radiography Prior to Third Molar Removal
ClinicalTrials.gov study NCT02071030. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Assess Structural Damage in Rheumatoid Arthritis Using Biomarkers and Radiography
ClinicalTrials.gov study NCT01476956. IPD Sharing: Not stated. Countries: 10. Publications: 2.
An Evaluation of a Self-contained Direct Digital Radiography System for Breast Specimen Imaging
ClinicalTrials.gov study NCT01379092. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Impact of Periapical Radiography and Cone Beam Computed Tomography on Periapical Assessment Following Surgical Endodontic Treatment
ClinicalTrials.gov study NCT04333940. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Comparative Study of Two Radiological Modalities, Ultrasonography Versus Stress Radiography, in the Urgent Care and Prognosis of Lateral Ankle Sprain (TALOS)
ClinicalTrials.gov study NCT00639028. IPD Sharing: Not stated. Countries: 1. Publications: 6.
Comparative Evaluation of an Electrical Impedance Device Versus Digital Radiography in Estimation of Remaining Dentin Thickness
ClinicalTrials.gov study NCT06162182. IPD Sharing: NO. Countries: 1. Publications: 1.
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.
Radiography Protocol in the Acute Phase After Proximal Femur Internal Fixation- Self Assessment and Recommendations
ClinicalTrials.gov study NCT02868125. IPD Sharing: NO. Countries: 1. Publications: 3.
Comparison of planar digital radiography and helical standing computed tomography for assessment of condylar stress fracture risk in Thoroughbred racehorses
Open the record for dataset details and reuse information.
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>
Time to Diagnosis of Glenohumeral Joint Dislocations in the ED- Traditional Radiography vs. POC Ultrasound
ClinicalTrials.gov study NCT05237167. IPD Sharing: NO. Countries: 0. Publications: 6.
Carestream Digital Radiography Long Length Imaging Software Data Collection Protocol
ClinicalTrials.gov study NCT01592435. IPD Sharing: NO. Countries: 1. Publications: 0.
Home mobile radiography service in the COVID-19 era
<p>We uploaded the image and dataset of the manuscript: Raiano N, Raiano C, Mazio F, Rossi I, Bordino U, De Simone G, Fusco R, Granata V, Cerciello V, Setola SV, Petrillo A. Home mobile radiography service in the COVID-19 era. Eur Rev Med Pharmacol Sci. 2021 Apr;25(8):3338-3341. doi: 10.26355/eurrev_202104_25745. PMID: 33928621.</p>
EBUS-TBNA-RTE VS Radiography in Staging of NSCLC
ClinicalTrials.gov study NCT03392506. IPD Sharing: Not stated. Countries: 1. Publications: 0.
On Previously Root Canal-treated Patients Using an AI Program, Detect the Accuracy of it in the Detection of Root Canal Obturation Quality on CBCT Compared With Conventional PA Radiography, e.g., Void
ClinicalTrials.gov study NCT07056998. IPD Sharing: YES. Countries: 1. Publications: 0.
Stereo Radiography of TKA Patella Mechanics
ClinicalTrials.gov study NCT02532933. IPD Sharing: Not stated. Countries: 0. Publications: 0.
A Standardized Novel Periapical Radiography Technique for Long Term Follow up of Implants Inserted Simultaneously With Sinus Allogenic Bone Augmentation
ClinicalTrials.gov study NCT06773637. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Comparison of Ultrasound Versus Radiography for Diagnosis of Nasal Fractures
ClinicalTrials.gov study NCT00650650. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Lung Ultrasound as Alternative to Radiography in Thoracic Surgery
ClinicalTrials.gov study NCT06261411. IPD Sharing: Not stated. Countries: 1. Publications: 0.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.