A deep learning integrated radiomics model for identification of coronavirus disease 2019 using computed tomography.

Since its first outbreak, Coronavirus Disease 2019 (COVID-19) has been rapidly spreading worldwide and caused a global pandemic. Rapid and early detection is essential to contain COVID-19. Here, we first developed a deep learning (DL) integrated radiomics model for end-to-end identification of COVID-19 using CT scans and then validated its clinical feasibility. We retrospectively collected CT images of 386 patients (129 with COVID-19 and 257 with other community-acquired pneumonia) from three medical centers to train and externally validate the developed models. A pre-trained DL algorithm was utilized to automatically segment infected lesions (ROIs) on CT images which were used for feature extraction. Five feature selection methods and four machine learning algorithms were utilized to develop radiomics models. Trained with features selected by L1 regularized logistic regression, classifier multi-layer perceptron (MLP) demonstrated the optimal performance with AUC of 0.922 (95% CI 0.856-0.988) and 0.959 (95% CI 0.910-1.000), the same sensitivity of 0.879, and specificity of 0.900 and 0.887 on internal and external testing datasets, which was equivalent to the senior radiologist in a reader study. Additionally, diagnostic time of DL-MLP was more efficient than radiologists (38 s vs 5.15 min). With an adequate performance for identifying COVID-19, DL-MLP may help in screening of suspected cases.

Characteristics of asymptomatic patients with SARS-CoV-2 infection in Jinan, China.

Coronavirus Disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2(SARS-CoV-2) is continuously and rapidly circulating at present. Asymptomatic patients have been proven to be contagious and thus pose a significant infection control challenge. Here we describe the characteristics of asymptomatic patients with SARS-CoV-2 infection in Jinan, Shandong province, China. A total of 47 patients with confirmed COVID-19 were recruited. Among them, 11 patients were categorized as asymptomatic cases. We found that the asymptomatic patients in Jinan were relatively young and were mainly clustered cases. The laboratory indicators and lung lesion on chest CT were mild. No special factors were found accounting for the presence or absence of symptoms. The presence of asymptomatic patients increased the difficulty of screening. It is necessary to strengthen the identification of such patients in the future.