Deep learning for automatic quantification of lung abnormalities in COVID-19 patients: First experience and correlation with clinical parameters

RATIONALE AND OBJECTIVES: To demonstrate the first experience of a deep learning-based algorithm for automatic quantification of lung parenchymal abnormalities in chest CT of COVID-19 patients and to correlate quantitative results with clinical and laboratory parameters. MATERIALS AND METHODS: We re...

Descripción completa

Detalles Bibliográficos
Autores principales: Mergen, Victor, Kobe, Adrian, Blüthgen, Christian, Euler, André, Flohr, Thomas, Frauenfelder, Thomas, Alkadhi, Hatem, Eberhard, Matthias
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7538094/
https://www.ncbi.nlm.nih.gov/pubmed/33043101
http://dx.doi.org/10.1016/j.ejro.2020.100272

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7538094/
https://www.ncbi.nlm.nih.gov/pubmed/33043101
http://dx.doi.org/10.1016/j.ejro.2020.100272

Ejemplares similares