A multicenter evaluation of a deep learning software (LungQuant) for lung parenchyma characterization in COVID-19 pneumonia

Abstract Background The role of computed tomography (CT) in the diagnosis and characterization of coronavirus disease 2019 (COVID-19) pneumonia has been widely recognized. We evaluated the performance of a software for quantitative analysis of chest CT, the LungQuant system, by comparing its results...

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Principais autores:	Camilla Scapicchio, Andrea Chincarini, Elena Ballante, Luca Berta, Eleonora Bicci, Chandra Bortolotto, Francesca Brero, Raffaella Fiamma Cabini, Giuseppe Cristofalo, Salvatore Claudio Fanni, Maria Evelina Fantacci, Silvia Figini, Massimo Galia, Pietro Gemma, Emanuele Grassedonio, Alessandro Lascialfari, Cristina Lenardi, Alice Lionetti, Francesca Lizzi, Maurizio Marrale, Massimo Midiri, Cosimo Nardi, Piernicola Oliva, Noemi Perillo, Ian Postuma, Lorenzo Preda, Vieri Rastrelli, Francesco Rizzetto, Nicola Spina, Cinzia Talamonti, Alberto Torresin, Angelo Vanzulli, Federica Volpi, Emanuele Neri, Alessandra Retico
Formato:	Artigo
Idioma:	Inglês
Publicado:	SpringerOpen 2023-04-01
Series:	European Radiology Experimental
Assuntos:	COVID-19 Deep Learning Lung Software validation Tomography (x-ray computed)
Acceso en liña:	https://doi.org/10.1186/s41747-023-00334-z
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