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

Abstract

Abstract 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.

Keywords

• COVID 19
• Coronavirus infections
• Quantitative CT analysis
• Qualitative CT analysis