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

Our raw COVID-19 dataset contained all the clinical data and the quantitative chest CT data. After excluding invalid and duplicate information, each sample contained 75 clinical data characteristics and a quantitative CT sequence obtained at different times. Since the sequence length of each sample varied from zero to seven, we adjusted the data structure of each sample to the same shape by zero-filling the uncollected or missing chest CT data. The original quantitative chest CT data contained twelve infection distribution features, eight infection sign type features, the thickness of thoracic diaphragm, and CT course. The lung was medically divided into 18 segments, and the infection sign characteristics at each checkpoint can be formatted as a matrix. This matrix composed of infection distribution features and sign type features was flattened into a vector and then concatenated with the original quantitative chest CT data.