{"id":5694,"date":"2023-09-04T03:18:49","date_gmt":"2023-09-04T03:18:49","guid":{"rendered":"https:\/\/cm.vastapps.dev\/tcia-collection\/covid-19-ny-sbu\/"},"modified":"2023-09-13T12:03:28","modified_gmt":"2023-09-13T12:03:28","slug":"covid-19-ny-sbu","status":"publish","type":"tcia_collection","link":"https:\/\/cm.vastapps.dev\/tcia-collection\/covid-19-ny-sbu\/","title":{"rendered":"COVID-19-NY-SBU"},"featured_media":8235,"template":"","citation-tax":[],"cancer_types":["COVID-19 (non-cancer)"],"citations":[4646,2925],"collection_doi":"10.7937\/TCIA.BBAG-2923","collection_download_info":"Click the Versions tab for more info about data releases.\nPlease contact help@cancerimagingarchive.net<\/a>\u00a0 with any questions regarding usage.","collection_downloads":[5278,5279,5280],"full_export":"

Summary<\/h1>

<\/span>

This collection of cases was acquired at Stony Brook University from patients who tested positive for COVID-19.\u00a0<\/span>The collection\u00a0<\/span>includes images from\u00a0<\/span>different modalities and organ sites (chest radiographs, chest CTs, brain MRIs, etc.). Radiology imaging\u00a0<\/span>data is extremely important in COVID-19 from both a diagnostic and a monitoring perspective, given the crucial nature of\u00a0<\/span>COVID-19 pulmonary disease and its rapid phenotypic changes. The datasets are available for building AI systems for\u00a0diagnostic\u00a0<\/span><\/span>and prognostic modeling.\u00a0<\/span><\/p>

This collection also includes associated clinical data for each patient. The clinical data <\/span>consists of diagnoses, procedures,\u00a0lab tests,\u00a0<\/span><\/span>covid19 specific data values (e.g., intubation status, symptoms at admission)\u00a0<\/span>and a set of derived data elements,\u00a0which were\u00a0<\/span>used <\/span>in analyses of this data. The clinical data is stored as a set of csv files <\/span>which comply with\u00a0OMOP Common Data\u00a0Model data elements.\u00a0<\/span><\/p>

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.<\/span><\/p>Acknowledgements<\/span><\/p>

Data collection was enabled by the Renaissance School of Medicine at Stony Brook University\u2019s \u201cCOVID-19 Data Commons and Analytic Environment\u201d, a data quality initiative instituted by the Office of the Dean, and supported by the Department of Biomedical Informatics.\u00a0<\/p>