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In Technology and health care : official journal of the European Society for Engineering and Medicine

BACKGROUND : Computed tomography (CT) imaging combined with artificial intelligence is important in the diagnosis and prognosis of lung diseases.

OBJECTIVE : This study aimed to investigate temporal changes of quantitative CT findings in patients with COVID-19 in three clinic types, including moderate, severe, and non-survivors, and to predict severe cases in the early stage from the results.

METHODS : One hundred and two patients with confirmed COVID-19 were included in this study. Based on the time interval between onset of symptoms and the CT scan, four stages were defined in this study: Stage-1 (0 ∼7 days); Stage-2 (8 ∼ 14 days); Stage-3 (15 ∼ 21days); Stage-4 (> 21 days). Eight parameters, the infection volume and percentage of the whole lung in four different Hounsfield (HU) ranges, ((-, -750), [-750, -300), [-300, 50) and [50, +)), were calculated and compared between different groups.

RESULTS : The infection volume and percentage of four HU ranges peaked in Stage-2. The highest proportion of HU [-750, 50) was found in the infected regions in non-survivors among three groups.

CONCLUSIONS : The findings indicate rapid deterioration in the first week since the onset of symptoms in non-survivors. Higher proportions of HU [-750, 50) in e lesion area might be a potential bio-marker for poor prognosis in patients with COVID-19.

Chen Xiaohui, Sun Wenbo, Xu Dan, Ma Jiaojiao, Xiao Feng, Xu Haibo


COVID-19, early detection, quantitative CT parameters, temporal changes