The proposed pulmonary inflammation load score was higher in patients with severe COVID-19 in comparison with patient with mild disease. The optimal inflammation load score threshold for identifying severe patients was 19.5, with 83.3% sensitivity and 94% specificity. Summary statement The chest CT severity score could be used to rapidly identify patients with severe forms of COVID-19. Background: Quantitative and semi-quantitative indicators to evaluate the severity of lung inflammation in Coronavirus Disease 2019 (COVID-19) could provide an objective approach to rapidly identify patients in need of hospital admission. Purpose: To evaluate the value of chest computed tomography severity score (CT-SS) in differentiating clinical forms of COVID-19. Materials and Methods: Inclusion of 102 patients with COVID-19 confirmed by positive real-time reverse transcriptase polymerase chain reaction on throat swabs underwent chest CT (53 men and 49 women, 15-79 years old, 84 cases with mild and 18 cases with severe disease). The CT-SS was defined by summing up individual scores from 20 lung regions; scores of 0, 1, and 2 were respectively assigned for each region if parenchymal opacification involved 0%, less than 50%, or equal or more than 50% of each region (theoretical range of CT-SS from 0 to 40). The clinical and laboratory data were collected, and patients were clinically subdivided according to disease severity by the Chinese National Health Commission guidelines. Results: The posterior segment of upper lobe (left, 68/102; right, 68/102), superior segment of lower lobe (left, 79/102; right, 79/102), lateral basal segment (left, 79/102; right, 70/102) and posterior basal segment of lower lobe (left, 81/102; right, 83/102) were the most frequently involved sites in COVID-19. Lung opacification mainly involved the lower lobes, in comparison with middle-upper lobes. No significant differences in distribution of the disease were seen between right and left lungs. The individual scores of in each lung, as well as the total CT-SS were higher in severe COVID-19 when compared with mild cases (P＜ 0.05. The optimal CT-SS threshold for identifying severe COVID-19 was 19.5 (area under curve, 0.892), with 83.3% sensitivity and 94% specificity. Conclusion: CT-SS could be used to quickly and objectively evaluate the severity of pulmonary involvement in COVID-19 patients.

We explored the relationships between lymphocyte subsets, cytokines, pulmonary inflammation index (PII) and disease evolution in patients with (corona virus disease 2019) COVID-19. A total of 123 patients with COVID-19 were divided into mild and severe groups. Lymphocyte subsets and cytokines were detected on the first day of hospital admission and lung computed tomography results were quantified by PII. Difference analysis and correlation analysis were performed on the two groups. A total of 102 mild and 21 severe patients were included in the analysis. There were significant differences in cluster of differentiation 4 (CD4 + T), cluster of differentiation 8 (CD8 + T), interleukin 6 (IL-6), interleukin 10 (IL-10) and PII between the two groups. There were significant positive correlations between CD4 + T and CD8 + T, IL-6 and IL-10 in the mild group (r 2 = 0Á694, r 2 = 0Á633, respectively; P < 0Á01). After 'five-in-one' treatment, all patients were discharged with the exception of the four who died. Higher survival rates occurred in the mild group and in those with IL-6 within normal values. CD4 + T, CD8 + T, IL-6, IL-10 and PII can be used as indicators of disease evolution, and the PII can be used as an independent indicator for disease progression of COVID-19.

Background: To achieve imaging report standardization and improve the quality and efficiency of the intrainterdisciplinary clinical workflow, we proposed an intelligent imaging layout system (IILS) for a clinical decision support system-based ubiquitous healthcare service, which is a lung nodule management system using medical images. Methods: We created a lung IILS based on deep learning for imaging report standardization and workflow optimization for the identification of nodules. Our IILS utilized a deep learning plus adaptive auto layout tool, which trained and tested a neural network with imaging data from all the main CT manufacturers from 11,205 patients. Model performance was evaluated by the receiver operating characteristic curve (ROC) and calculating the corresponding area under the curve (AUC). The clinical application value for our IILS was assessed by a comprehensive comparison of multiple aspects. Findings: Our IILS is clinically applicable due to the consistency with nodules detected by IILS, with its highest consistency of 0•94 and an AUC of 90•6% for malignant pulmonary nodules versus benign nodules with a sensitivity of 76•5% and specificity of 89•1%. Applying this IILS to a dataset of chest CT images, we demonstrate performance comparable to that of human experts in providing a better layout and aiding in diagnosis in 100% valid images and nodule display. The IILS was superior to the traditional manual system in performance, such as reducing the number of clicks from 14•45 ± 0•38 to 2, time consumed from 16•87 ± 0•38 s to 6•92 ± 0•10 s, number of invalid images from 7•06 ± 0•24 to 0, and missing lung nodules from 46•8% to 0%. Interpretation: This IILS might achieve imaging report standardization, and improve the clinical workflow therefore opening a new window for clinical application of artificial intelligence. Fund: The National Natural Science Foundation of China.

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