Deep learning for predicting COVID-19 malignant progression

作者：Cong Fang, Song Bai, Qianlan Chen, Yu Zhou, Liming Xia, Lixin Qin, Shi Gong, Xudong Xie, Chunhua Zhou, Dandan Tu, Changzheng Zhang, Xiaowu Liu, Weiwei Chen, Xiang Bai, Philip H. S. Torr · 发表于：medRxiv · 年份：2020 · DOI：10.1101/2020.03.20.20037325 · 被引用次数：71 · 研究领域：COVID-19 diagnosis using AI、Machine Learning in Healthcare、Radiomics and Machine Learning in Medical Imaging

Abstract As COVID-19 is highly infectious, many patients can simultaneously flood into hospitals for diagnosis and treatment, which has greatly challenged public medical systems. Treatment priority is often determined by the symptom severity based on first assessment. However, clinical observation suggests that some patients with mild symptoms may quickly deteriorate. Hence, it is crucial to identify patient early deterioration to optimize treatment strategy. To this end, we develop an early-warning system with deep learning techniques to predict COVID-19 malignant progression. Our method leverages clinical data and CT scans of outpatients and achieves an AUC of 0.920 in the single-center study and an average AUC of 0.874 in the multicenter study. Moreover, our model automatically identifies crucial indicators that contribute to the malignant progression, including Troponin, Brain natriuretic peptide, White cell count, Aspartate aminotransferase, Creatinine, and Hypersensitive C-reactive protein.