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COVID-19 disease: CT Pneumonia Analysis prototype by using artificial intelligence, predicting the disease severity

Gouda, Walaa ; Yasin, Rabab

Egyptian Journal of Radiology and Nuclear Medicine, 2020-09, Vol.51 (1), p.196-11, Article 196 [Periódico revisado por pares]

Berlin/Heidelberg: Springer Berlin Heidelberg

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  • Título:
    COVID-19 disease: CT Pneumonia Analysis prototype by using artificial intelligence, predicting the disease severity
  • Autor: Gouda, Walaa ; Yasin, Rabab
  • Assuntos: Algorithms ; Artificial intelligence ; Automation ; Bacterial pneumonia ; Coronavirus infections ; Coronaviruses ; COVID 19 ; CT imaging ; Decision-making ; Diagnostic imaging ; Disease transmission ; Imaging ; Lungs ; Males ; Medical research ; Medicine ; Medicine & Public Health ; Medicine, Experimental ; Nuclear Medicine ; Performance evaluation ; Pneumonia ; Qualitative CT analysis ; Quantitative CT analysis ; Radiology ; Severe acute respiratory syndrome coronavirus 2 ; Variables
  • É parte de: Egyptian Journal of Radiology and Nuclear Medicine, 2020-09, Vol.51 (1), p.196-11, Article 196
  • Descrição: Background Since the beginning of 2020, coronavirus disease has spread widely all over the world and this required rapid adequate management; therefore, continuous searching for rapid and sensitive CT chest techniques was needed to give a hand for the clinician. We aimed to assess the validity of computed tomography (CT) quantitative and qualitative analysis in COVID-19 pneumonia and how it can predict the disease severity on admission. Results One hundred and twenty patients were enrolled in our study, 98 (81.7%) of them were males, and 22 (18.3%) of them were females with a mean age of 52.63 ± 12.79 years old, ranging from 28 to 83 years. Groups B and C showed significantly increased number of involved lung segments and lobes, frequencies of consolidation, crazy-paving pattern, and air bronchogram. The total lung severity score and the total score for crazy-paving and consolidation are used as severity indicators in the qualitative method and could differentiate between groups B and C and group A (90.9% sensitivity, 87.5% specificity, and 93.2% sensitivity, 87.5% specificity, respectively), while the quantitative indicators could differentiate these three groups. Using the quantitative CT indicators, the validity to differentiate different groups showed 84.1% sensitivity and 81.2% specificity for the opacity score, and 90.9% sensitivity and 81.2% specificity for the percentage of high opacity. Conclusion Advances in CT COVID-19 pneumonia assessment provide an accurate and rapid tool for severity assessment, helping for decision-making notably for the critical cases.
  • Editor: Berlin/Heidelberg: Springer Berlin Heidelberg
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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