Application of deep learning-based diagnostic systems in screening asymptomatic COVID-19 patients among oversea returnees.

INTRODUCTION: Our study aimed to investigate the performance of deep learning (DL)-based diagnostic systems in alerting against COVID-19, especially among asymptomatic individuals coming from overseas, and to analyze the features of identified asymptomatic patients in detail. METHODOLOGY: DL diagnostic systems were deployed to assist in the screening of COVID-19, including the pneumonia system and pulmonary nodules system. 1,917 overseas returnees who underwent CT examination and rRT-PCR tests were enrolled. DL pneumonia system promptly alerted clinicians to suspected COVID-19 after CT examinations while the performance was evaluated with rRT-PCR results as the reference. The radiological features of asymptomatic COVID-19 cases were described according to the Nomenclature of the Fleischner Society. RESULTS: Fifty-three cases were confirmed as COVID-19 patients by rRT-PCR tests, including 5 asymptomatic cases. DL pneumonia system correctly alerted 50 cases as suspected COVID-19 with a sensitivity of 0.9434 and specificity of 0.9592 (within 2 minutes per case); while the pulmonary nodules system alerted 2 of the 3 missed asymptomatic cases. Additionally, five asymptomatic patients presented different characteristics such as elevated creatine kinase level and prolonged prothrombin time, as well as atypical radiological features. CONCLUSIONS: DL diagnostic systems are promising complementary approaches for prompt screening of imported COVID-19 patients, even the imported asymptomatic cases. Unique clinical and radiological characteristics of asymptomatic cases might be of great value in screening as well. ADVANCES IN KNOWLEDGE: DL-based systems are practical, efficient, and reliable to assist radiologists in screening COVID-19 patients. Differential features of asymptomatic patients might be useful to clinicians in the frontline to differentiate asymptomatic cases.

Moderate vs. mild cases of overseas-imported COVID-19 in Beijing: a retrospective cohort study.

This study compared the differences in the clinical manifestations, treatment courses and clinical turnover between mild and moderate coronavirus disease 2019 (COVID-19). Clinical data of the patients with imported COVID-19 admitted to Beijing Xiaotangshan Designated Hospital between March 15 and April 30, 2020, were retrospectively analysed. A total of 53 COVID-19 patients were included, with 21 mild and 32 moderate cases. Compared with the mild group, the moderate group showed significant differences in breathing frequency, lymphocyte count, neutrophil percentage, neutrophil/lymphocyte ratio, procalcitonin, C-reactive protein, and dynamic erythrocyte sedimentation rate. In the moderate group, 87.5% exhibited ground-glass opacities, 14% exhibited consolidative opacities, 53.1% exhibited local lesions and 68.8% exhibited unilateral lesions. The proportion of patients who received antiviral or antibiotic treatment in the moderate group was higher than that in the mild group, and the number of cases that progressed to severe disease in the moderate group was also significantly higher (18.7% vs. 0%, p = 0.035). Compared with patients with mild COVID-19, those with moderate COVID-19 exhibited more noticeable inflammatory reactions, more severe pulmonary imaging manifestations and earlier expression of protective antibodies. The overall turnover of the moderate cases was poorer than that of the mild cases.