Progress of artificial intelligence diagnosis and prognosis technology for COVID-19 medical imaging

ABSTRACT

In March 2020, the World Health Organization(WHO) declared the new corona virus pneumonia (COVID-19) as a world pandemic, which means that the epidemic has broken out worldwide. The outbreak of COVID-19 threatens the lives and property safety of countless people and brings great pressure to medical systems. The main clinical symptoms of COVID-19 are fever, cough, and fatigue, which may lead to a fatal complication acute respiratory distress syndrome. The main challenge in inhibiting the spread of this disease is the lack of efficient detection methods. Although reverse transcription-polymerase chain reaction (RT-PCR) is the gold standard for confirming COVID-19, it takes 4-6 h to obtain the results, and the false-negative rate of RT-PCR detection is as high as 17%-25.5%. Therefore, multiple RT-PCR detections at intervals of several days must be performed to confirm the diagnostic result. In addition, RT-PCR reagents are lacking in many severe epidemic areas. By contrast, X-ray and CT(computed tomography) examination equipment have been widely popularized in hospitals. In clinical practice, by combining clinical symptoms and travel history, CT is an efficient and safe method to diagnose COVID-19. Compared with CT, X-ray examination has faster scanning speed and lower radiation amount. Moreover, X-ray and CT images are important tools for doctors to track and observe the condition and evaluate the efficacy. In summary, medical imaging plays a vital role in limiting the spread of viruses and treating COVID-19. During the outbreak of the epidemic, medical imaging-based AI-assisted diagnostic technology has become a popular research direction. Computer-aided diagnostic technology improves the sensitivity and specificity of doctors' diagnosis and is accurate and efficient, which helps rapid diagnosis of a large number of suspicious cases. For example, the out preformed AI-assisted diagnosis system can achieve an accuracy rate comparable to that of radiologists, and it take less than 1 second to perform a diagnosis. The system has been used in 16 hospitals, with more than 1 300 diagnoses performed daily. This article reviews the latest research works on AI-assisted diagnosis of COVID-19 and analyzes and summarizes them on the basis of four aspects data preparation, image segmentation, diagnosis, and prognosis. First, this article organizes some public data sets to support the AI-assisted diagnostic technology of COVID-19 and provides several solutions to insufficient datasets, such as the human-in-the-loop strategy, which improves the efficiency of data set production. Using transfer learning, weakly supervised or unsupervised learning can reduce model's dependence on the COVID-19 dataset. Second, the semantic segmentation network is also an indispensable part of the intelligent diagnosis of COVID-19. Segmenting the lung region from the original image is a key pre-processing step, which can reduce the calculation amount of subsequent algorithms. The lesion area helps the doctor to track the condition of the disease, and the infection rate can be calculated according to the size of the infected area. U-Net, U-Net++, and attention U-Net are suitable for the segmentation of medical images because of the small number of parameters, which is not easy to overfit. Furthermore, training the semantic segmentation network with the idea of the generative adversarial network (GAN) can improve the Dice coefficient. Third, this article introduces the AI diagnostic system from two aspects of CT images and X-rays. Comparing different diagnostic schemes, the method of diagnosis based on the segmentation images is better than that based on the original images. Among the classification networks, ResNet and VGG19(visual geometry group 19-layer net) perform better. Methods such as GAN, location attention mechanisms, transfer learning, and combining 2D and 3D features can be used to improve accuracy. In addition, clinical information (travel and contact history, white blood cell count, fever, cough, sputum, patient age, and patient gender) c n be used as a basis for diagnosis. For example, algorithm D_FF_Conic uses clinical information as a diagnostic basis and has reached an accuracy rate of 90%. Clinicians will consider medical imaging and clinical information in the process of diagnosis, but the current AI diagnostic system cannot integrate multiple types of data for diagnosis. Although some algorithms can fuse the diagnostic results of medical images with the diagnostic results of clinical information, the simple fine-tuned algorithm haven't learned the deep internal connection between different types of data. Fourth, AI technology can also predict high-risk patients on the basis of infection rates and clinical information. Some research predicted the survival rate of COVID-19 patients on the basis of age, syndrome, and infection rate. Such algorithms can help doctors find and treat high-risk patients early, thereby reducing mortality, which is of great significance. This article shows the latest progress of COVID-19's medical imaging-based AI diagnosis. Although some AI-assisted diagnostic systems have been deployed in hospitals to play a practical role, these algorithms still have some problems, such as insufficient training data, a single diagnostic basis, and the ability to distinguish between non-COVID-19 pneumonia and COVID-19. © 2020, Editorial and Publishing Board of Journal of Image and Graphics. All right reserved.