Abstract:
Objective: To evaluate the correlation of CT artificial intelligence quantitative analysis in prediction and evaluation of clinical classification of COVID-19 pneumonia. Methods: The clinical and CT imaging data of 46 patients with COVID-19 treated in the hospital from February 1st, 2019 to January 20th, 2021 was retrospectively analyzed. We compared the correlation between the total lung infection volume, grinding glass density volume (GGO volume), solid density volume (SO volume) and clinical classification when artificial intelligence (AI) quantitative analysis was applied. Results: Among the 26 cases of common type, 16 cases of severe type and 4 cases of critical type, the main clinical manifestations were fever, cough and fatigue. Severe and critical types were more common in elder patients. The CT manifestations of three clinical types of pulmonary lesions were mainly GGO; total lung infection volume, GGO volume, SO volume in common type were smaller than that in patients of severe/critical type. Spearman grade correlation analysis showed significant correlation between total lung infection volume, GGO volume, and SO volume with clinical classification (0.86, 0.87, 0.84). Conclusion: The artificial intelligence CT quantitative index analysis (infection volume, GGO volume, SO volume) holds much correlation with clinical classification of COVID-19 pneumonia.