Summary
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Summary as of 6/15/21: Thecollection of cases was acquired at Stony Brook University from patients who tested positive for COVID-19. The collectioncollection includes images fromfrom different modalities and organ sites (chest radiographs, chest CTs, brain MRIs, etc.). Radiology imagingimaging data is extremely important in COVID-19 from both a diagnostic and a monitoring perspective, given the crucial nature ofof COVID-19 pulmonary disease and its rapid phenotypic changes. The datasets are available for non-commercial use by research communitiesfor building AI systems for diagnosticfor diagnostic and prognostic modeling. (KG & JK, should this be part of the Summary or is this topic covered in the Licensing Agreement on the Citation Tab?) Data collection was enabled by the Renaissance School of Medicine at Stony Brook University’s “COVID-19 Data Commons and Analytic Environment”, a data quality initiative instituted by the Office of the Dean, and supported by the Department of Biomedical Informatics. (KG & JK, this seems more like an Acknowledgement. What do you think? We can also check with the site.)
This collection also includes associated Original Summary: We have curated clinical and imaging data for covid19-positive patients admitted to the SBU hospital. The dataset consists of de-identified Radiology imaging data along with linkedclinical data for each patient. The clinical data consists of diagnoses, procedures, lab tests, covid19 specific data values (e.g., intubation status, symptoms at admission) and a set of derived data elements, which werewhich were used in analyses of this data. The clinical data is stored as a set of csv files which comply with OMOPwith OMOP Common Data ModelData Model data elements. Additional questions site was asked to address: Please address the following items in your revision:
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Acknowledgements
We would like to acknowledge the individuals and institutions that have provided data for this collection:
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Hospital/Institution Name city, state, country - Special thanks to First Last Names, degree PhD, MD, etc from the Department of xxxxxx, Additional Names from same location.
The images on the right show automated identification of regions of prognostic importance on baseline chest radiographs. The regions of highest prognostic importance (as determined by the AI algorithm) are observed primarily in lower lung regions, consistent with clinical findings on the corresponding CXRs. |
Acknowledgements
Data collection was enabled by the Renaissance School of Medicine at Stony Brook University’s “COVID-19 Data Commons and Analytic Environment”, a data quality initiative instituted by the Office of the Dean, and supported by the Department of Biomedical Informatics.
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