Artificial intelligence-based approaches for COVID-19 patient management.

During the highly infectious pandemic of coronavirus disease 2019 (COVID-19), artificial intelligence (AI) has provided support in addressing challenges and accelerating achievements in controlling this public health crisis. It has been applied in fields varying from outbreak forecasting to patient management and drug/vaccine development. In this paper, we specifically review the current status of AI-based approaches for patient management. Limitations and challenges still exist, and further needs are highlighted.

Thin-Section Chest CT Imaging of COVID-19 Pneumonia: A Comparison Between Patients with Mild and Severe Disease.

PURPOSE: To compare radiologic characteristics of coronavirus disease 2019 (COVID-19) pneumonia at thin-section CT on admission between patients with mild and severe disease. MATERIALS AND METHODS: Seventy patients with COVID-19 pneumonia who were admitted to Zhongnan Hospital of Wuhan University between January 20, 2020 and January 27, 2020 were enrolled. On the basis of the World Health Organization guidelines, 50 patients were categorized with the mild form and 20 with the severe form based on clinical conditions. Imaging features, clinical, and laboratory data were reviewed and compared. RESULTS: Patients with the severe form (median age, 65 years; interquartile range [IQR]: 54.75-75.00 years) were older than those with the mild form of disease (median age, 42.5 years; IQR: 32.75-58.50 years) (P < .001). Patients with the severe form of disease had more lung segments involved (median number of segments: 17.5 vs 7.5, P ≤ .001) and also larger opacities (median number of segments with opacities measuring 3 cm to less than 50% of the lung segment: 5.5 vs 2.0, P = .006; ≥ 50% of lung segment: 7.5 vs 0.0, P < .001). They also had more interlobular septal thickening (75% vs 28%, P < .001), higher prevalence of air bronchograms (70% vs 32%, P = .004), and pleural effusions (40% vs 14%, P = .017). CONCLUSION: Ground-glass opacities with or without consolidation in a peripheral and basilar predominant distribution were the most common findings in COVID-19 pneumonia. Patients with the severe form of the disease had more extensive opacification of the lung parenchyma than did patients with mild disease. Interlobular septal thickening, air bronchograms, and pleural effusions were also more prevalent in severe COVID-19.© RSNA, 2020.

Early CT Findings of Coronavirus Disease 2019 (COVID-19) in Asymptomatic Children: A Single-Center Experience.

OBJECTIVE: The current study reported a case series to illustrate the early computed tomography (CT) findings of coronavirus disease 2019 (COVID-19) in pediatric patients. MATERIALS AND METHODS: All pediatric patients who were diagnosed with COVID-19 and who underwent CT scan in Zhongnan Hospital of Wuhan University from January 20, 2020 to February 28, 2020 were included in the current study. Data on clinical and CT features were collected and analyzed. RESULTS: Four children were included in the current study. All of them were asymptomatic throughout the disease course (ranging from 7 days to 15 days), and none of them showed abnormalities in blood cell counts. Familial cluster was the main transmission pattern. Thin-section CT revealed abnormalities in three patients, and one patient did not present with any abnormal CT findings. Unilateral lung involvement was observed in two patients, and one patient showed bilateral lung involvement. In total, five small lesions were identified, including ground-glass opacity (n = 4) and consolidation (n = 1). All lesions had ill-defined margins with peripheral distribution and predilection of lower lobe. CONCLUSION: Small patches of ground-glass opacity with subpleural distribution and unilateral lung involvement were common findings on CT scans of pediatric patients in the early stage of the disease.

Prediction of the Development of Pulmonary Fibrosis Using Serial Thin-Section CT and Clinical Features in Patients Discharged after Treatment for COVID-19 Pneumonia

OBJECTIVE: To identify predictors of pulmonary fibrosis development by combining follow-up thin-section CT findings and clinical features in patients discharged after treatment for COVID-19. MATERIALS AND METHODS: This retrospective study involved 32 confirmed COVID-19 patients who were divided into two groups according to the evidence of fibrosis on their latest follow-up CT imaging. Clinical data and CT imaging features of all the patients in different stages were collected and analyzed for comparison. RESULTS: The latest follow-up CT imaging showed fibrosis in 14 patients (male, 12;female, 2) and no fibrosis in 18 patients (male, 10;female, 8). Compared with the non-fibrosis group, the fibrosis group was older (median age: 54.0 years vs. 37.0 years, p = 0.008), and the median levels of C-reactive protein (53.4 mg/L vs. 10.0 mg/L, p = 0.002) and interleukin-6 (79.7 pg/L vs. 11.2 pg/L, p = 0.04) were also higher. The fibrosis group had a longer-term of hospitalization (19.5 days vs. 10.0 days, p = 0.001), pulsed steroid therapy (11.0 days vs. 5.0 days, p < 0.001), and antiviral therapy (12.0 days vs. 6.5 days, p = 0.012). More patients on the worst-state CT scan had an irregular interface (59.4% vs. 34.4%, p = 0.045) and a parenchymal band (71.9% vs. 28.1%, p < 0.001). On initial CT imaging, the irregular interface (57.1%) and parenchymal band (50.0%) were more common in the fibrosis group. On the worst-state CT imaging, interstitial thickening (78.6%), air bronchogram (57.1%), irregular interface (85.7%), coarse reticular pattern (28.6%), parenchymal band (92.9%), and pleural effusion (42.9%) were more common in the fibrosis group. CONCLUSION: Fibrosis was more likely to develop in patients with severe clinical conditions, especially in patients with high inflammatory indicators. Interstitial thickening, irregular interface, coarse reticular pattern, and parenchymal band manifested in the process of the disease may be predictors of pulmonary fibrosis. Irregular interface and parenchymal band could predict the formation of pulmonary fibrosis early.