Objective To analyze variations in the chest CT of different clinical outcomes of corona virus disease 2019 (COVID-19) based on deep learning and improve the understanding on COVID-19 imaging.

Methods The chest CT of 42 cases (the clinical classification was common type) of COVID-19 in Inner Mongolia Autonomous Region collected from January 25, 2020 to March 29, 2020 were examined. The cases included 20 males and 22 females, with ages ranging from 17 months to 86 (48.74±17.18) years. The patients were divided into group A, which included those did not progress to severe disease (n=29), and group B, which included those who progressed to severe/critical disease (n=13). Differences in age, gender, lesion distribution, sides, number, volume, density (CT value), and proportion of lesion volume as detected by chest CT were compared between the two groups by deep learning. Two independent samples t test and Mann-Whitney U nonparametric tests were used to compare measurement data, and the χ² and Fisher's exact tests were used to compare count data.

Results No statistical difference in gender was noted between the two groups (χ²=0.016, P=1.000). The mean age of group B was higher than that of group A (65.62±11.24 years vs. 41.17±13.66 years), and a statistical difference was observed in each group (t=5.64, P<0.001). The number of infected pulmonary lobes, proportion of infection volume in each pulmonary lobe, and proportion of total infection volume were higher in group B than in group A, and a statistical difference was noted in each group (Z=2.505−3.605, all P<0.05). No statistical difference in total lung volume between the two groups was observed (Z=1.456, P=0.146). The size of infection in each lobe and the total volume of infection in both lungs in group B were greater than those in group A, and a statistical difference was found in each group (Z=2.301−3.254, all P<0.05). The proportion of lesions in group B within the threshold range of all CT values was higher than that in group A, and a statistical difference was observed in each group (Z=3.115−3.578, all P<0.05). The results of chest CT and artificial intelligence lesion recognition mapping revealed that lesions in serious cases are mainly characterized with ground glass opacity and consolidation. Moreover, the lesions frequently involved the lower lobes of the lungs and less commonly affected the middle lobe of the right lung.

Conclusions The chest CT of patients with COVID-19 who progressed to severe disease and those who did not showed significant differences. Artificial intelligence based on deep learning can assess patients with a tendency to progress to severe/critical disease early and contribute to the improved management of severe COVID-19.