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Temporal changes of quantitative CT findings from 102 patients with COVID-19 in Wuhan, China: A longitudinal study.
- Source :
-
Technology and health care : official journal of the European Society for Engineering and Medicine [Technol Health Care] 2021; Vol. 29 (S1), pp. 297-309. - Publication Year :
- 2021
-
Abstract
- Background: Computed tomography (CT) imaging combined with artificial intelligence is important in the diagnosis and prognosis of lung diseases.<br />Objective: This study aimed to investigate temporal changes of quantitative CT findings in patients with COVID-19 in three clinic types, including moderate, severe, and non-survivors, and to predict severe cases in the early stage from the results.<br />Methods: One hundred and two patients with confirmed COVID-19 were included in this study. Based on the time interval between onset of symptoms and the CT scan, four stages were defined in this study: Stage-1 (0 ∼7 days); Stage-2 (8 ∼ 14 days); Stage-3 (15 ∼ 21days); Stage-4 (> 21 days). Eight parameters, the infection volume and percentage of the whole lung in four different Hounsfield (HU) ranges, ((-, -750), [-750, -300), [-300, 50) and [50, +)), were calculated and compared between different groups.<br />Results: The infection volume and percentage of four HU ranges peaked in Stage-2. The highest proportion of HU [-750, 50) was found in the infected regions in non-survivors among three groups.<br />Conclusions: The findings indicate rapid deterioration in the first week since the onset of symptoms in non-survivors. Higher proportion of HU [-750, 50) in the lesion area might be a potential bio-marker for poor prognosis in patients with COVID-19.
- Subjects :
- Adult
Aged
Aged, 80 and over
COVID-19 mortality
China
Comorbidity
Disease Progression
Female
Humans
Lung diagnostic imaging
Male
Middle Aged
Prognosis
Retrospective Studies
SARS-CoV-2
Severity of Illness Index
Time Factors
Artificial Intelligence
COVID-19 diagnostic imaging
COVID-19 physiopathology
Tomography, X-Ray Computed methods
Subjects
Details
- Language :
- English
- ISSN :
- 1878-7401
- Volume :
- 29
- Issue :
- S1
- Database :
- MEDLINE
- Journal :
- Technology and health care : official journal of the European Society for Engineering and Medicine
- Publication Type :
- Academic Journal
- Accession number :
- 33682766
- Full Text :
- https://doi.org/10.3233/THC-218027