An Interpretable Chest CT Deep Learning Algorithm for Quantification of COVID-19 Lung Disease and Prediction of Inpatient Morbidity and Mortality

RATIONALE AND OBJECTIVES: The burden of coronavirus disease 2019 (COVID-19) airspace opacities is time consuming and challenging to quantify on computed tomography. The purpose of this study was to evaluate the ability of a deep convolutional neural network (dCNN) to predict inpatient outcomes assoc...

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Detalles Bibliográficos
Autores principales: Chamberlin, Jordan H., Aquino, Gilberto, Schoepf, Uwe Joseph, Nance, Sophia, Godoy, Franco, Carson, Landin, Giovagnoli, Vincent M., Gill, Callum E., McGill, Liam J., O'Doherty, Jim, Emrich, Tilman, Burt, Jeremy R., Baruah, Dhiraj, Varga-Szemes, Akos, Kabakus, Ismail M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Association of University Radiologists. Published by Elsevier Inc. 2022
Materias:
Original Investigation
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8977389/
https://www.ncbi.nlm.nih.gov/pubmed/35610114
http://dx.doi.org/10.1016/j.acra.2022.03.023

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8977389/
https://www.ncbi.nlm.nih.gov/pubmed/35610114
http://dx.doi.org/10.1016/j.acra.2022.03.023

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