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1,087 results for “Chest”
N-terminal Pro B-type Natriuretic Peptide (Nt-proBNP) Versus Exercise Test for Evaluation of Acute Chest Pain
ClinicalTrials.gov study NCT00493844. IPD Sharing: NO. Countries: 1. Publications: 5.
Non-contrast Cardiac CT as a Risk Stratification Tool in Patients With Non-cardiac Chest Pain
ClinicalTrials.gov study NCT02422316. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Women With Chest Pain and no Significant Coronary Artery Stenosis; A Study on Microvascular Resistance
ClinicalTrials.gov study NCT01582165. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Code for: Comparison and interpretability of machine learning models to predict severity of chest injury
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Data from: Genetic structuring and secondary contact in the white-chested Amazilia hummingbird species complex
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A systematic review on the effects of high frequency chest wall compression and intrapulmonary percussive ventilation in patients with neuromuscular disease
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ChatGPT Provides Inconsistent Risk-Stratification of Patients With Atraumatic Chest Pain - Dataset
<p>Dataset for analysis of the ability of ChatGPT to risk-stratify patients with atraumatic chest pain</p>
Automated detection, segmentation and classification of pericardial effusions on chest CT using a deep convolutional neural network
<p>Trainingsdata for chest CT pericard effusion and the finish trained nnU-Net model. </p>
Giant sengi or elephant-shrew (Rhynchocyon species) interactions with Red-Capped Robin-Chat (Cossypha natalensis) and White-Chested Alethe (Chamaetylas fuelleborni) in Tanzania
<p>Using camera traps and direct observations, we recorded interactions between three species of giant sengi (<em>Rhynchocyon sp.</em>) and two insectivorous bird species from five forests in Tanzania. In all instances, the birds closely followed the giant sengis who were moving and foraging in the leaf litter. Given the two bird species are insectivorous and follow ant swarms to obtain flushed prey, these interactions suggest that following the sengi represents an adaptive foraging strategy. Our observations indicate that these behaviors and associations may be more pervasive than previously thought.</p>
Chest tomb, St Lawrence's church , Eyam
Created with Polycam Source: Objaverse 1.0 / Sketchfab
RSNA Chest X-ray Images
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VinDr WhatsApp Chest X-ray Dataset
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Figure 1 from: Cai J, Yu W-B, Zhang T, Wang H, Li D-Z (2019) China's biodiversity hotspots revisited: A treasure chest for plants. In: Cai J, Yu W-B, Zhang T, Li D-Z (Eds) Revealing of the plant diversity in China's biodiversity hotspots. PhytoKeys 130: 1-24. https://doi.org/10.3897/phytokeys.130.38417
Figure 1 Chart of new species reported to occur in China's biodiversity hotspots, based on data from IPNI, 1 Jan. 2013- 31 Dec. 2018
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>
Automated detection, segmentation and classification of pericardial effusions on chest CT using a deep convolutional neural network
<p>Test data set</p>
Emergency Small vs Large Tube Thoracostomy in Chest Trauma Patients.
ClinicalTrials.gov study NCT04863989. IPD Sharing: Not stated. Countries: 0. Publications: 12.
Effectiveness of Ranolazine on Reducing Chest Pain in Patients With Blockage But no Stents
ClinicalTrials.gov study NCT02147834. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Aggrastat to Zocor (AtoZ) - the Use of Two Approved Drugs to Treat Patients Who Have Experienced Chest Pain or a Heart Attack (0733-180)
ClinicalTrials.gov study NCT00251576. IPD Sharing: Not stated. Countries: 0. Publications: 7.
Ranolazine for the Treatment of Chest Pain in HCM Patients
ClinicalTrials.gov study NCT01721967. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Is Non-Cardiac Chest Pain Caused by Sustained Longitudinal Smooth Muscle Contraction?
ClinicalTrials.gov study NCT01839058. 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.