Abstract：【Objective】To explore the CT imaging features of patients infected with novel coronavirus Omicron variant,and to quantitatively score the total severity of lesions by artificial intelligence. 【Methods】The clinical data and chest CT imaging characteristics of 23 patients diagnosed with Omicron variant (B.1.1.529) of coronavirus in Huaihua First People's Hospital from March 17 to March 26,2022 were retrospectively analyzed. The volume and proportion of pneumonia were automatically identified and semi-quantitatively calculated by artificial intelligence software to evaluate the application in clinical classification/severity grading. 【Results】Of the 23 patients with Omicron variant of coronavirus,8 cases were positive on chest CT first,which included 6 cases of focal ground-glass opacities and 2 cases of nodular consolidation in the lung.Lesions were mainly distributed in the middle zone of the lung. Five patients had single lobe involvement,of which,4 cases had single lobe involvement in the left lung,1 in the right lung. There were 3 cases involoved in both lungs. No pleural effusion and lymph node enlargement were found in all 8 patients. After artificial intelligence analysis showed that all 8 patients with positive CT examination had the total CT severity score less than or equal to 5 points,including 4 cases with a score of 1 point,1 case with a score of 2 points,2 cases with 3 a score of points and 1 case with a score of 5 points. All patients had mild symptoms,and the CT value of some patients decreased significantly before and after treatment. 【Conclusion】The main imaging manifestations of Omicron variant of coronavirus infection are focal ground-glass opacities and consolidation nodules,which are mainly distributed in the middle zone of the single lung lobe and segment. The prognosis is good,the rate of asymptomatic infection is high,and the degree of pneumonia is mild. The image analyzed by artificial intelligence quantitative characteristics of Omicron variant infection are of great significance for early identification and effective treatment.
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2023, Vol. 40 
