| Autor: |
P. D’alessio, L. Stortoni, I. Galdino, C. A. Mallio, A. Mattei, C. C. Quattrocchi, F. E. Agrò, B. Gallì, E. Di Giorgio, M. G. Donatiello |
| Rok vydání: |
2021 |
| Předmět: |
|
| Zdroj: |
Journal of Xiangya Medicine. 6:3-3 |
| ISSN: |
2519-9390 |
| Popis: |
Background: Vascular findings in coronavirus disease 2019 (COVID-19) are not systematically described using 384-row state-of-the-art chest CT angiography (CTA). The relationship between CT-CTA features and arterial blood gas (ABG) parameters is not fully understood. Methods: Chest CT images were acquired with Dual Source 384-slice (2×192) CT (Siemens SOMATOM Force). Quantitative volumetric assessment of lung lesions and the CT severity score were calculated by using a deep learning algorithm trained on COVID-19 pneumonia and correlated with ABG parameters. Assessment of pulmonary vascular tree was performed on CTA images. Statistical analysis included Mann-Whitney U test and non-parametric Spearman’s Rho test, with significance threshold at P |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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