Clinical evaluation of SARS-CoV-2 lung HRCT and RT-PCR Techniques: Towards risk factor based diagnosis of infectious diseases.
Age
COVID-19
Coronaviruses
Gender
HRCT
RT-PCR
Journal
Computational and structural biotechnology journal
ISSN: 2001-0370
Titre abrégé: Comput Struct Biotechnol J
Pays: Netherlands
ID NLM: 101585369
Informations de publication
Date de publication:
2021
2021
Historique:
received:
10
12
2020
revised:
24
04
2021
accepted:
24
04
2021
pubmed:
11
5
2021
medline:
11
5
2021
entrez:
10
5
2021
Statut:
ppublish
Résumé
This study uses image analysis techniques for comparative analysis of the lung HRCT features and RT-PCR of 325 suspected patients to COVID-19 pneumonia. Our findings propose more caution in the interpretation of RT-PCR data, promoting, instead, also the quantification of age and sex-based risk factors using HRCT images. Statistical analysis of our methodology reveals a direct relation between intensity, skewness and kurtosis of the radiological features and the gender of patients. Moreover, we investigate the effect of the age of patients on the appearance of COVID-19 pneumonia in the HRCT images. We have also applied our methodology to investigate the effect of time on the severity of COVID-19 pneumonia within the lungs. Subsequently, we find a strong relationship between image analysis and the informed medical diagnosis asserted by the radiologists. Additionally, our results also indicate increase in the severity of lung infection in the first and second week after the onset of the SARS-CoV-2 symptoms. Thereafter, a gradual decrease in the lung damage is observed during the third week. The proposed image analysis methodology can be used as a simple complementary tool for infectious disease diagnostics as demonstrated in this study with an example of SARS-CoV-2 to provide better understanding of the disease for drug and vaccine development.
Identifiants
pubmed: 33968332
doi: 10.1016/j.csbj.2021.04.058
pii: S2001-0370(21)00170-7
pmc: PMC8084916
doi:
Types de publication
Journal Article
Langues
eng
Pagination
2699-2707Informations de copyright
© 2021 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.
Déclaration de conflit d'intérêts
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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