Helping Roles of Artificial Intelligence (AI) in the Screening and Evaluation of COVID-19 Based on the CT Images
Authors Xie H, Li Q, Hu PF, Zhu SH, Zhang JF, Zhou HD, Zhou HB
Received 14 January 2021
Accepted for publication 3 March 2021
Published 26 March 2021 Volume 2021:14 Pages 1165—1172
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Professor Ning Quan
Hui Xie,1,2 Qing Li,2,3 Ping-Feng Hu,4 Sen-Hua Zhu,5 Jian-Fang Zhang,6 Hong-Da Zhou,1 Hai-Bo Zhou4
1Department of Radiation Oncology, Affiliated Hospital (Clinical College) of Xiangnan University, Chenzhou, 423000, People’s Republic of China; 2Key Laboratory of Medical Imaging and Artificial Intelligence of Hunan Province, Chenzhou, 423000, People’s Republic of China; 3Department of Interventional Vascular Surgery, Affiliated Hospital (Clinical College) of Xiangnan University, Chenzhou, 423000, People’s Republic of China; 4Department of Radiology, The Second People’s Hospital of Chenzhou City, Chenzhou, 423000, People’s Republic of China; 5Beijing Linking Medical Technology Co., Ltd, Beijing, 100085, People’s Republic of China; 6Department of Physical Examination, Disease Control and Prevention of Chenzhou, Chenzhou, 423000, People’s Republic of China
Correspondence: Qing Li
Department of Interventional Vascular Surgery, Affiliated Hospital (Clinical College) of Xiangnan University, 25 Renmin Street, Chenzhou, 423000, People’s Republic of China
Tel +86 19918761912
Email [email protected]
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.
Keywords: CT, COVID-19, intelligent analysis, AI, helping role
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