Vaccination effect on patients with Delta variant of COVID-19 pneumonia: a study of longitudinal dynamic chest CTs using artificial intelligence model

Background The Delta variant of COVID-19 has emerged and spread globally since May 2021 and has been reported in more than 70 countries. The status of the vaccination, symptom onset time, and CT imaging signatures in the infected population have not been fully investigated and clarified. Methods This study included 131 patients who were infected with the Delta variant of COVID-19. After screening, 106 patients with 458 follow-up CT scans were retrospectively selected and divided into complete and incomplete vaccination groups (66 and 40 patients, respectively). Imaging features were automatically extracted, and infection distribution in lung fields and progression pattern tendency were investigated. Furthermore, we extracted the most related clinical and imaging features to establish a vaccination status classification model. An independent testing dataset with 55 patients from another inpatient ward was enrolled to evaluate the generalizability of the model. Results The severity of infection in the lung and lung fields of the complete vaccination group was overall lower than those of the incomplete vaccination group. A relatively earlier peak CT abnormality was found on days 8–11 in the complete vaccination group than in the incomplete vaccination group on days 12–15 after the first positive PCR time. The vaccination status classification model achieved the highest performance with an AUC of 0.929 and accuracy of 0.864 in the testing set and an AUC of 0.858 and accuracy of 0.727 in the independent testing set. Conclusion In summary, compared to the incomplete vaccination group, the fully vaccinated patients had milder CT abnormalities and earlier peak time for chest impairment. Therefore, the vaccination status is determinable through dynamic imaging and clinical features.