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suck abstract from ncbi


10.1016/j.csbj.2021.04.058

http://scihub22266oqcxt.onion/10.1016/j.csbj.2021.04.058
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33968332!8084916!33968332
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suck abstract from ncbi


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pmid33968332      Comput+Struct+Biotechnol+J 2021 ; 19 (ä): 2699-2707
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  • Clinical evaluation of SARS-CoV-2 lung HRCT and RT-PCR Techniques: Towards risk factor based diagnosis of infectious diseases #MMPMID33968332
  • Asadi F; Shahnazari R; Bhalla N; Payam AF
  • Comput Struct Biotechnol J 2021[]; 19 (ä): 2699-2707 PMID33968332show ga
  • 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.
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