A Survey on Artificial Intelligence in Chest Imaging of COVID-19

The coronavirus disease 2019 (COVID-19) is caused by the severe acute respiratory syndrome (SARS) coronavirus 2 (CoV-2), and has strong infectiousness, with over 9.3 million confirmed cases and about 0.47 million deaths worldwide as of June 24, 2020. In the clinical diagnosis of this disease, chest computed tomography (CT) and X-ray scans provide a supplement for real-time reverse-transcription polymerase chain reaction (RT-PCR) testing. As mentioned in [1], according to RT-PCR results, the sensitivity and accuracy of the disease infection on chest CT images are 97% and 68%, respectively. However, conventional diagnosis and analysis methods for CT/Xray scans need a lot of manual labor and cost a large amount of time, leading to a huge burden to radiologists. To overcome these difficulties and improve the accuracy of diagnosis, artificial intelligence (AI) techniques based on deep learning have recently attracted extensive interests of researchers due to the superiority of AI in medical imaging analysis. Currently, deep learning-based AI has been widely used in the medical imaging field as using it can achieve significantly better performance than traditional methods. The works in [2] and [3] provide a comprehensive overview of deep learning methods in medical imaging analysis. For the localization and identification of thoracic diseases, Li et al. [4] applied the popular ResNet architecture introduced in [5] to build a computational model that can perform two such tasks simultaneously. With respect to the anatomical region of the chest, a technique based on a threedimensional (3D) fully convolutional network (FCN) was proposed for the registration of lung CT inspiration–expiration image pairs in [6]. To study the categorization of focal/diffuse lung opacities in chest X-ray (CXR) images, Brestel et al. [7] presented a convolutional neural network (CNN)-based technique called RadBotCXR, and claimed that their method achieved the level of radiologists for this task. Moreover, Ozturk et al. [8] applied a hand-crafted feature-based model for chest imaging analysis, where a stacked autoencoder or principal component analysis (PCA) was applied to decrease the dimensions of the resulting feature vector, and the support vector machine (SVM) with a kernel was trained as the classifier. AI-assisted chest imaging analysis methods have been studied to alleviate the pressure of radiologists and improve the efficiency of COVID-19 diagnosis. Dong et al. [9] investigated the role of imaging in 1School of Mathematics and Computational Science, Xiangtan University, Xiangtan, China