Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
15
datasets available to search
ShareScore release 0.9.0
Dataset results
15 results for “Chest Radiographs”
Comparison of different Lunit INSIGHT CXR software versions when reading chest radiographs for tuberculosis
Open the record for dataset details and reuse information.
Chest Radiograph at Diverse Institutes (CRADI) dataset
<p><strong>Introduction to the Chest Radiograph at Diverse Institutes (CRADI) dataset</strong></p> <p> </p> <p><strong>Background</strong></p> <p> </p> <p>Chest radiography is extensively used to screen and diagnose pulmonary and cardiac diseases. The advantages of its clinical practicality, efficiency, and cost-effectiveness make chest radiography the most accessible imaging test for pulmonary disorders, especially in primary hospitals. Currently, the interpretation of a chest radiograph mainly relies on radiologists.</p> <p> </p> <p>With the development of algorithms, convolutional neural networks (CNNs) have shown the ability to detect a single disorder in chest radiography, e.g., pneumothorax, lung cancer, pneumonia, and tuberculosis. Traditionally, expert annotation is applied to establish a CNN model for classifying medical images. This manual labeling procedure is time-consuming and highly demanding. Importantly, beyond the detection of a single disease, multi-label classification is necessary to interpret a chest radiograph in clinical practice.</p> <p> </p> <p>In order to promote the development of the artificial intelligence-assisted diagnosis of chest radiography, we launched the Chest Radiograph at Diverse Institutes (CRADI) dataset. This dataset is comprised of a large number of chest radiographs. Each radiograph has a 25-label disorder annotation, that was established by the terms adopted from the Fleischner’s glossary, and extracted from the original diagnostic report by natural language processing (NLP) and radiologist expertise.</p> <p> </p> <p>At present, the data of the CRADI dataset comes from two academic hospitals and multiple community clinics in Shanghai. The cases in the CRADI dataset are comprised of in-patients, out-patients, and screening participants.</p> <p> </p> <p>The CRADI dataset provides a better understanding of the multiple and different clinical data sources for chest radiography, which is potentially helpful for the training and test of CNN models.</p> <p> </p> <p>We welcome more data into the CRADI dataset. If you find it helpful to your research work or you want to contribute to this dataset, please feel free to contact us.</p> <p> </p> <p><strong>Data source</strong></p> <p><strong>Number of images</strong></p> <p><strong>Data_source</strong></p> <p>Academic hospital 1</p> <p>74,082</p> <p>0</p> <p>In- and out-patient from Academic hospital 2</p> <p>5,996</p> <p>1</p> <p>Screening participants from Academic hospital 2</p> <p>2,130</p> <p>2</p> <p>Community clinics</p> <p>1,804</p> <p>3</p> <p>Note. Each case includes one posterior-anterior (PA) view chest radiograph and the corresponding text label of disorder findings.</p> <p> </p> <p> </p> <p> </p> <p><strong>Preprocessing methods</strong></p> <p>Images: transformed and resized from DCM format to PNG, changed from 12-bit grayscale to 8-bit. All patient- or institute-related information is de-identified.</p> <p>Label: labels are extracted from the original diagnostic reports. The regular expression is applied by NLP and rules-based extraction methods. In total, 25 labels are extracted for each image.</p> <p><strong>Data</strong></p> <p><strong>Overview: </strong>All images are compressed into one file. All classification labels are listed in one CSV file. Data order is as following way:</p> <p><strong>Data organization</strong></p> <p><strong>Classification result</strong></p> <p>Each image links to the label by an item of ‘patientID’.</p> <p>data_resource<a href="#_msocom_1">[1]</a> stands for the resource of data. Data sources are listed in the previous table.</p> <p>Result table format: |pateintID|data_resource<a href="#_msocom_2">[2]</a> |label1|label2|.......|label25|</p> <p>The order of the 25 labels is as the following:</p> <p>1) pneumothorax, 2) emphysema, 3) pulmonary parenchymal calcification, 4) PICC implant, 5) aortic unfolding, 6) aortic arteriosclerosis, 7) aortic abnormalities, 8) small consolidation, 9) cardiomegaly, 10) patchy consolidation, 11) consolidation, 12) cavity, 13) mass, 14) prominent bronchovascular marking, 15) pulmonary edema, 16) pulmonary nodule, 17) hilar adenopathy, 18) pleural effusion, 19) pleural thickening, 20) pleural adhesion, 21) pleural calcification, 22) pleural abnormalities, 23) scoliosis, 24) pacemaker implant, 25) interstitial involvement.</p> <p>Data_resource or data_source?</p> <p>同上</p> <p><strong>Introduction to the Chest Radiograph at Diverse Institutes (CRADI) dataset</strong></p> <p> </p> <p><strong>Background</strong></p> <p> </p> <p>Chest radiography is extensively used to screen and diagnose pulmonary and cardiac diseases. The advantages of its clinical practicality, efficiency, and cost-effectiveness make chest radiography the most accessible imaging test for pulmonary disorders, especially in primary hospitals. Currently, the interpretation of a chest radiograph mainly relies on radiologists.</p> <p> </p> <p>With the development of algorithms, convolutional neural networks (CNNs) have shown the ability to detect a single disorder in chest radiography, e.g., pneumothorax, lung cancer, pneumonia, and tuberculosis. Traditionally, expert annotation is applied to establish a CNN model for classifying medical images. This manual labeling procedure is time-consuming and highly demanding. Importantly, beyond the detection of a single disease, multi-label classification is necessary to interpret a chest radiograph in clinical practice.</p> <p> </p> <p>In order to promote the development of the artificial intelligence-assisted diagnosis of chest radiography, we launched the Chest Radiograph at Diverse Institutes (CRADI) dataset. This dataset is comprised of a large number of chest radiographs. Each radiograph has a 25-label disorder annotation, that was established by the terms adopted from the Fleischner’s glossary, and extracted from the original diagnostic report by natural language processing (NLP) and radiologist expertise.</p> <p> </p> <p>At present, the data of the CRADI dataset comes from two academic hospitals and multiple community clinics in Shanghai. The cases in the CRADI dataset are comprised of in-patients, out-patients, and screening participants.</p> <p> </p> <p>The CRADI dataset provides a better understanding of the multiple and different clinical data sources for chest radiography, which is potentially helpful for the training and test of CNN models.</p> <p> </p> <p>We welcome more data into the CRADI dataset. If you find it helpful to your research work or you want to contribute to this dataset, please feel free to contact us.</p> <p><strong>Data source </strong></p> <p>training data data source: 0</p> <p>In- and out-patient from external hospital data source: 1</p> <p>Screening participants from external hospital data source : 2</p> <p>Community clinics datasource: 3</p> <p>Note. Each case includes one posterior-anterior (PA) view chest radiograph and the corresponding text label of disorder findings.</p> <p><strong>Preprocessing methods</strong></p> <p>Images: transformed and resized from DCM format to PNG, changed from 12-bit grayscale to 8-bit. All patient- or institute-related information is de-identified.</p> <p>Label: labels are extracted from the original diagnostic reports. The regular expression is applied by NLP and rules-based extraction methods. In total, 25 labels are extracted for each image.</p> <p><strong>Data</strong></p> <p><strong>Overview: </strong>All images are compressed into one file. All classification labels are listed in one CSV file. Data order is as following way:</p> <p><strong>Classification result</strong></p> <p>Each image links to the label by an item of ‘patientID’.</p> <p>data_resource<a href="#_msocom_1">[1]</a> stands for the resource of data. Data sources are listed in the previous table.</p> <p>Result table format: |pateintID|data_resource<a href="#_msocom_2">[2]</a> |label1|label2|.......|label25|</p> <p>The order of the 25 labels is as the following:</p> <p>1) pneumothorax, 2) emphysema, 3) pulmonary parenchymal calcification, 4) PICC implant, 5) aortic unfolding, 6) aortic arteriosclerosis, 7) aortic abnormalities, 8) small consolidation, 9) cardiomegaly, 10) patchy consolidation, 11) consolidation, 12) cavity, 13) mass, 14) prominent bronchovascular marking, 15) pulmonary edema, 16) pulmonary nodule, 17) hilar adenopathy, 18) pleural effusion, 19) pleural thickening, 20) pleural adhesion, 21) pleural calcification, 22) pleural abnormalities, 23) scoliosis, 24) pacemaker implant, 25) interstitial involvement.</p> <p>Data will be released after anonymilization process.</p>
Comparison of Two Chest Radiograph Prescription Strategies in Intensive Care Unit
ClinicalTrials.gov study NCT00893672. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Reader Study of DeltaView™ Chest Radiograph Software
ClinicalTrials.gov study NCT01261507. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Outcomes Following Omission of Daily Routine Chest Radiographs Following Pulmonary Resection
ClinicalTrials.gov study NCT03704870. IPD Sharing: NO. Countries: 1. Publications: 0.
A Clinical Study of TRUVIEW ART™(Advanced Reconstruction Tech)to Improve Image Quality on Chest Radiograph
ClinicalTrials.gov study NCT02971917. IPD Sharing: YES. Countries: 1. Publications: 0.
xrAI - Improving Quality and Efficiency in Chest Radiograph Interpretation
ClinicalTrials.gov study NCT04153045. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Clinical Validation of Machine Learning Triage of Chest Radiographs
ClinicalTrials.gov study NCT05224479. IPD Sharing: NO. Countries: 1. Publications: 0.
Accuracy of the Chest Radiograph for the Diagnosis of Acute Respiratory Distress Syndrome
ClinicalTrials.gov study NCT07400445. IPD Sharing: YES. Countries: 1. Publications: 0.
Evaluation of IGRA, Symptoms and Chest Radiograph for Subclinical TB Detection in ESRD and KT
ClinicalTrials.gov study NCT06700876. IPD Sharing: NO. Countries: 1. Publications: 0.
Coronavirus: Ventilator Outcomes Using Artificial Intelligence Chest Radiographs & Other Evidence-based Co-variates
ClinicalTrials.gov study NCT04855539. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Convolutional Neural Network Model to Detect Coronavirus Disease 2019 (COVID-19) Pneumonia in Chest Radiographs
ClinicalTrials.gov study NCT05722665. IPD Sharing: NO. Countries: 1. Publications: 0.
Osteoporotic Precisely Screening Using Chest Radiograph and Artificial Neural Network (OPSCAN)
ClinicalTrials.gov study NCT05721157. IPD Sharing: NO. Countries: 1. Publications: 0.
xrAI - Improving Quality and Efficiency in Chest Radiograph Interpretation by Radiologists
ClinicalTrials.gov study NCT04221100. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.
phase-contrast chest radiographs
<p>see <a href="https://doi.org/10.5281/zenodo.7030750">https://doi.org/10.5281/zenodo.7030750</a></p>
ScienceDex guides
Understand access before you commit
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.