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Background  Coronavirus disease 2019 (COVID-19) has caused a global pandemic that has raised worldwide concern. This study aims to investigate the correlation between the extent of lung infection and relevant clinical laboratory testing indicators in COVID-19 and to analyse its underlying mechanism.    Methods  Chest high-resolution computer tomography (CT) images and laboratory examination data of 31 patients with COVID-19 were extracted, and the lesion areas in CT images were quantitatively segmented and calculated using a deep learning (DL) system. A cross-sectional study method was carried out to explore the differences among the proportions of lung lobe infection and to correlate the percentage of infection (POI) of the whole lung in all patients with clinical laboratory examination values.    Results  No significant difference in the proportion of infection was noted among various lung lobes ( P  &gt; 0.05). The POI of total lung was negatively correlated with the peripheral blood lymphocyte percentage (L%) ( r  = − 0.633,  P  &lt; 0.001) and lymphocyte (LY) count ( r  = − 0.555,  P  = 0.001) but positively correlated with the neutrophil percentage (N%) ( r  = 0.565,  P  = 0.001). Otherwise, the POI was not significantly correlated with the peripheral blood white blood cell (WBC) count, monocyte percentage (M%) or haemoglobin (HGB) content. In some patients, as the infection progressed, the L% and LY count decreased progressively accompanied by a continuous increase in the N%.    Conclusions  Lung lesions in COVID-19 patients are significantly correlated with the peripheral blood lymphocyte and neutrophil levels, both of which could serve as prognostic indicators that provide warning implications, and contribute to clinical interventions in patients.    Supplementary Information  The online version contains supplementary material available at 10.1186/s12879-021-05839-9.

bmc infectious diseases

Correlation between lung infection severity and clinical laboratory indicators in patients with COVID-19: a cross-sectional study based on machine learning