CT imaging has become an effective tool for screening COVID-19 patients and assessing the severity of COVID-19 infection [17-22]. Considering that there is still no effective medicine to treat COVID-19, clinicians use different degrees of supportive therapy to intervene disease and then to see how disease progresses. To evaluate the therapeutic response in confirmed COVID-19 inpatients, follow-up CT examinations are required every 3 to 5 days. However, radiologists and clinicians still do not have a computerized tool to accurately quantify the treatment response. Deep learning (DL) has become a popular method in medical image analysis [23, 24], and it has been used to screen for and diagnose COVID-19 on CT images [15, 25, 26]. Li et al.  used a DL model to train a large chest CT dataset; their results showed sensitivity of 90%, specificity of 96%, and accuracy of 0.96 in identifying COVID-19. The accurate segmentation by the DL system is a basis for quantitative assessment of CT images [15, 27, 28], which is necessary to track the progression of the disease and analyze longitudinal changes of COVID-19 throughout the treatment period [29, 30]. In this study, we included the data from 14 patients with mild-type COVID-19 and evaluated longitudinally and quantitatively the changes on chest CT during COVID-19 by using a tool of a DL-based network (VB-Net). We believe that this DL-based CT system for COVID-19 quantification can facilitate clinical decisions on treatment.
VB-Net system is an application of image processing used for segmentation of the lung, lung lobes, and lung infection . It can provide accurate quantitative data for medical research, including quantitative assessment of disease progression at follow-up, comprehensive evaluation of severity, visualization and quantification of the lesion distribution using percentage of infection (POI). Shi et al.  used the VB-Net system to compute chest CT images of 2685 patients, and showed sensitivity of 90.7%, specificity of 83.3%, and accuracy of 87.9%. We used the VB-Net system to quantify longitudinal changes between the initial and follow-up CT scans of COVID-19 patients. Figure 2 shows a case of a 57-year-old COVID-19 patient with five follow-up CT scans. The changes in infection volume as well as GGOs and consolidation components were clearly visualized by using the infection region segmentation method and surface rendering technique.
Dynamic radiological features on chest CT images of COVID-19 have already been reported . In this study, we found that the distribution of lesions at the initial CT scan was predominantly in the bilateral lower lobes in 14 patients. GGOs accounted for the largest volume of the lesions, and some lesions showed consolidation changes and fibrosis, consistent with the results reported in previous studies [33-38]. Early fibrosis may correlate with good prognosis . Six patients showed an increase in consolidation component at the first follow-up, and two of them showed progression as reflected in increased infected area of multiple lobes and denser consolidation components. Finally, consolidation components were gradually absorbed. We also showed that the components in the lesions could be identified by CT number. Namely, for all lesions, the VB-Net system can calculate the POI with a CT number in the predefined range. In this study, we found the highest POI for the CT number in the range <−300 HU, indicating that GGOs were major components of the lesions. These chest CT radiological features were consistent with COVID-19. The overall POIs of 14 patients showed that the range of CT number gradually decreased. These results indicate that the CT features of COVID-19 infected lesions can be visually displayed by using the DL-based CT system, which might help clinicians to manage patients with COVID-19.
Shan et al.  indicated that the POI estimated from CT scans correlated with the severity of pneumonia. With the VB-Net system, the POIs of the whole lung and lung lobes can be automatically calculated, and then, the severity of COVID-19 infection in the whole lung and each lobe can be quantified. Table 2 shows that the POI of the right lower lobe was higher than that of the other lobes, which is in agreement with the findings reported in previous studies [36-38]. In addition, the research works of DL also can be helpful in predicting COVID-19 disease progression. Cao et al.  have reported that the use of voxel-level DL-based CT segmentation of pulmonary opacities can improve their quantification and assess longitudinal progression of COVID-19. Huang et al.  collected CT images from 126 patients and calculated the percentage of GGOs in chest CT images; they found that the quantification of the lung infection could be further used for monitoring of the progression of COVID-19. In our study, the whole lung and lesions were segmented by VB-Net system, and the POIs were calculated from follow-up CT scans of 14 COVID-19 patients. Our results suggested that the POIs of most patients showed a gradual downward trend after antiviral and supportive treatment (Figure 1). These follow-up results suggest that clinically relevant supportive treatment can be reflected in changes in POIs. Therefore, our study illustrates the potential of DL-based CT system to provide objective quantitative assessment of pulmonary infection as well as of response to treatment in patients with COVID-19.
In summary, DL plays an important role in the delineation of infected lesions and quantification of COVID-19 applications. It helps radiologists in accurately identifying lung infection and prompting quantitative analysis and diagnosis of COVID-19.
In addition, the discharge criteria of common COVID-19 patients were managed well by DL; however, six cases in our study showed “re-positive” results during the follow-up nucleic acid tests. The quantitative CT follow-up results showed that the absorption of the infected lesions gradually decreased. Currently, there are research reports on COVID-19 “re-positive” patients without worsening symptoms and chest CT findings. Additionally, the patients were isolated at the infection ward of the hospital and were not exposed to other confirmed or suspected patients, which indicated that these “re-positive” tests were not the result of a re-infection. After obtaining the “re-positive” results, in the six patients, the samples from multiple sites, including the nasopharynx, throat, and anal swabs, were tested for COVID-19 nucleic acid for more than 3 days, but all the test results were negative. The quantitative results of CT before discharge showed that the reduction rate of POI in the lung lesions was more than 50%, and even the infected lesions in those six patients with re-positive tests were completely absorbed. After discharge, all the patients underwent 14 days of isolation and health surveillance at home.
To ensure that patients are completely cured, analysis of IgM and IgG COVID-19-specific antibodies should be carried out for all discharged patients. Additionally, the CT scans of “re-positive” patients still showed abnormal lesions in the lungs at the follow-up visit, suggesting that more rigorous criteria are needed to evaluate the CT results so as to reduce the possibility of “re-positive” test results.
This study had several limitations. First, although DL has become an effective method in fighting against COVID-19, the CT data in COVID-19 patients may be incomplete and inaccurate, which creates difficulty in training an accurate segmentation and diagnostic network. Meanwhile, our current AI study for quantification of the lesions was based on a small sample, which may have led to overfitting of the results. Multicenter prospective studies with larger samples need to be conducted to further verify the conclusions of the present study. Additionally, our study did not analyze the COVID-19-specific IgG- and IgM antibodies due to the unavailability of such data. Finally, more datasets should be established to include clinically collected CT images from patients with COVID-19.