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Artificial Intelligence Clinicians Can Use Chest Computed Tomography Technology to Automatically Diagnose Coronavirus Disease 2019 (COVID-19) Pneumonia and Enhance Low-Quality Images

Authors Zhang Q, Chen Z, Liu G, Zhang W, Du Q, Tan J, Gao Q

Received 8 December 2020

Accepted for publication 21 January 2021

Published 24 February 2021 Volume 2021:14 Pages 671—687

DOI https://doi.org/10.2147/IDR.S296346

Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 4

Editor who approved publication: Professor Suresh Antony


Quan Zhang,1,2 Zhuo Chen,1 Guohua Liu,1,2 Wenjia Zhang,1,2 Qian Du,1,2 Jiayuan Tan,1,2 Qianqian Gao1,2

1College of Electronic Information and Optical Engineering, Nankai University, Tianjin, 300350, People’s Republic of China; 2Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology, Nankai University, Tianjin, 300350, People’s Republic of China

Correspondence: Guohua Liu Tel +86-22-85358565
Email liugh@nankai.edu.cn

Purpose: Nowadays, the number of patients with COVID-19 pneumonia worldwide is still increasing. The clinical diagnosis of COVID-19 pneumonia faces challenges, such as the difficulty to perform RT-PCR tests in real time, the lack of experienced radiologists, clinical low-quality images, and the similarity of imaging features of community-acquired pneumonia and COVID-19. Therefore, we proposed an artificial intelligence model GARCD that uses chest CT images to assist in the diagnosis of COVID-19 in real time. It can show better diagnostic performance even facing low-quality CT images.
Methods: We used 14,129 CT images from 104 patients. A total of 12,929 samples were used to build artificial intelligence models, and 1200 samples were used to test its performance. The image quality improvement module is based on the generative adversarial structure. It improves the quality of the input image under the joint drive of feature loss and content loss. The enhanced image is sent to the disease diagnosis model based on residual convolutional network. It automatically extracts the semantic features of the image and then infers the probability that the sample belongs to COVID-19. The ROC curve is used to evaluate the performance of the model.
Results: This model can effectively enhance the low-quality image and make the image that is difficult to be recognized become recognizable. The model proposed in this paper reached 97.8% AUC, 96.97% sensitivity and 91.16% specificity in an independent test set. ResNet, GADCD, CNN, and DenseNet achieved 80.9%, 97.3%, 70.7% and 85.7% AUC in the same test set, respectively. By comparing the performance with related works, it is proved that the model proposed has stronger clinical usability.
Conclusion: The method proposed can effectively assist doctors in real-time detection of suspected cases of COVID-19 pneumonia even faces unclear image. It can quickly isolate patients in a targeted manner, which is of positive significance for preventing the further spread of COVID-19 pneumonia.

Keywords: coronavirus disease 2019, artificial intelligence, low-quality image enhancement, auxiliary diagnosis, deep learning

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