Predicting COVID-19 malignant progression with AI techniques

Xiang Bai; Cong Fang; Yu Zhou; Song Bai; Zaiyi Liu; Qianlan Chen; Yongchao Xu; Tian Xia; Shi Gong; Xudong Xie; Dejia Song; Ronghui Du; Chunhua Zhou; Chengyang Chen; Dianer Nie; Dandan Tu; Changzheng Zhang; Xiaowu Liu; Lixin Qin; Weiwei Chen

Lesions and imaging features were assessed in each lung segment of each patient. The number of involved segments was counted not only for each patient or each lobe but also for each imaging feature. If more than one type of imaging features present in a segment, the segment was counted for every involved feature. The imaging features assessed in this study included 1) ground glass opacity (GGO); 2) consolidation; 3) air bronchogram; 4) paving stone sign; 5) fibrosis; 6) nodule; and 7) halo sign. The first available CT after symptoms onset, the follow-up CT, the first available CT of the severe stage were assigned as CT 1 , CT 2 ~ CT n , and CT severe separately. In order to compare the longitudinal variation of CT features during the period of CT 1 and CT severe between the two patient groups, we chose CT 2 instead of CT severe for those patients without severe/critical progression.