Objective The purpose of this study was to compare imaging features between COVID-19 and mycoplasma pneumonia (MP). Materials and methods The data of patients with mild COVID-19 and MP who underwent chest computed tomography (CT) examination from February 1, 2020 to April 17, 2020 were retrospectively analyzed. The Pneumonia-CT-LKM-PP model based on a deep learning algorithm was used to automatically quantify the number, volume, and involved lobes of pulmonary lesions, and longitudinal changes in quantitative parameters were assessed in three CT follow-ups. Results A total of 10 patients with mild COVID-19 and 13 patients with MP were included in this study. There was no difference in lymphocyte counts at baseline between the two groups (1.43 ± 0.45 vs. 1.44 ± 0.50, p = 0.279). C-reactive protein levels were significantly higher in MP group than in COVID-19 group ( p < 0.05). The number, volume, and involved lobes of pulmonary lesions reached a peak in 7–14 days in the COVID-19 group, but there was no peak or declining trend over time in the MP group ( p < 0.05). Conclusion Based on the longitudinal changes of quantitative CT, pulmonary lesions peaked at 7–14 days in patients with COVID-19, and this may be useful to distinguish COVID-19 from MP and evaluate curative effects and prognosis.
【저자키워드】 COVID-19, Mycoplasma pneumonia, Quantitative CT, 【초록키워드】 Prognosis, deep learning, Pneumonia, C-reactive protein, Computed tomography, Chest computed tomography, Lymphocyte count, lymphocyte, Algorithm, Patient, Mild, Quantitative, curative effects, longitudinal changes, Volume, two groups, no difference, lobe, pulmonary lesions, COVID-19 group, lymphocyte counts, material, lobes, parameter, Effect, objective, feature, longitudinal change, Result, analyzed, was used, evaluate, involved, peaked, significantly higher, reached, two group, pulmonary lesion, automatically, baseline, curative, declining, patients with COVID-19, were assessed, 【제목키워드】 Pneumonia,