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

With the worldwide outbreak of COVID-19, early prediction and early aggressive treatment of mild patients at high risk of malignant progression to severe/critical stage are important ways to reduce mortality. In this work, we found that the complementarity of clinical data and quantitative chest CT sequence is important for predicting patients with malignant progression. In particular, the rich series information of the chest CT sequence, which has not been considered by other studies so far, is critical for this specific task. We also demonstrated that our method can effectively fuse these two complementary data and handle time-series information in the quantitative chest CT sequence, which achieved an AUC of 0.954 (95% CI Although lots of clinical, laboratory, and imaging parameters varied significantly between patients with and without severe/critical progression, seven predictive All rights reserved. No reuse allowed without permission.