Value and prognostic impact of a deep learning segmentation model of COVID-19 lung lesions on low-dose chest CT

OBJECTIVES: 1) To develop a deep learning (DL) pipeline allowing quantification of COVID-19 pulmonary lesions on low-dose computed tomography (LDCT). 2) To assess the prognostic value of DL-driven lesion quantification. METHODS: This monocentric retrospective study included training and test dataset...

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Detalles Bibliográficos
Autores principales: Bartoli, Axel, Fournel, Joris, Maurin, Arnaud, Marchi, Baptiste, Habert, Paul, Castelli, Maxime, Gaubert, Jean-Yves, Cortaredona, Sebastien, Lagier, Jean-Christophe, Million, Matthieu, Raoult, Didier, Ghattas, Badih, Jacquier, Alexis
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Authors. Published by Elsevier Masson SAS on behalf of Société française de radiologie. 2022
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8939894/
https://www.ncbi.nlm.nih.gov/pubmed/37520010
http://dx.doi.org/10.1016/j.redii.2022.100003

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8939894/
https://www.ncbi.nlm.nih.gov/pubmed/37520010
http://dx.doi.org/10.1016/j.redii.2022.100003

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