Helping Roles of Artificial Intelligence (AI) in the Screening and Evaluation of COVID-19 Based on the CT Images

Abstract

Objective The aim of this study was to explore the role of the AI system which was designed and developed based on the characteristics of COVID-19 CT images in the screening and evaluation of COVID-19. Methods The research team adopted an improved U-shaped neural network to segment lungs and pneumonia lesions in CT images through multilayer convolution iterations. Then the appropriate 159 cases were selected to establish and train the model, and Dice loss function and Adam optimizer were used for network training with the initial learning rate of 0.001. Finally, 39 cases (29 positive and 10 negative) were selected for the comparative test. Experimental group: an attending physician a and an associate chief physician a read the CT images to diagnose COVID-19 with the help of the AI system. Control group: an attending physician b and an associate chief physician b did the diagnosis only by their experience, without the help of the AI system. The time spent by each doctor in the diagnosis and their diagnostic results were recorded. Paired t -test, univariate ANOVA, chi-squared test, receiver operating characteristic curves, and logistic regression analysis were used for the statistical analysis. Results There was statistical significance in the time spent in the diagnosis of different groups ( P <0.05). For the group with the optimal diagnostic results, univariate and multivariate analyses both suggested no significant correlation for all variables, and thus it might be the assistance of the AI system, the epidemiological history and other factors that played an important role. Conclusion The AI system developed by us, which was created due to COVID-19, had certain clinical practicability and was worth popularizing.

Network Intelligent Education System Based on the Deep Learning Algorithm

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The severity assessment and nucleic acid turning-negative-time prediction in COVID-19 patients with COPD using a fused deep learning model

“…The time of nucleic acid conversion to negative was closely related to the clinical manifestations and disease progression of COVID-19 patients [20]. With the wide spread of models based on arti cial intelligence, automatic extraction of quantitative metrics based on deep learning method from lung computed tomography (CT) could provide the tool to the screening and evaluation of COVID-19 [21][22]. In addition, radiomics has been widely used in quantitative analysis of CT images for screening COVID-19 patients [23][24], discriminating the severity of pneumonia [25], and prognosis assessment in patients with COVID-19 pneumonia [26].…”