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In European radiology ; h5-index 62.0

OBJECTIVES : Early recognition of coronavirus disease 2019 (COVID-19) severity can guide patient management. However, it is challenging to predict when COVID-19 patients will progress to critical illness. This study aimed to develop an artificial intelligence system to predict future deterioration to critical illness in COVID-19 patients.

METHODS : An artificial intelligence (AI) system in a time-to-event analysis framework was developed to integrate chest CT and clinical data for risk prediction of future deterioration to critical illness in patients with COVID-19.

RESULTS : A multi-institutional international cohort of 1,051 patients with RT-PCR confirmed COVID-19 and chest CT was included in this study. Of them, 282 patients developed critical illness, which was defined as requiring ICU admission and/or mechanical ventilation and/or reaching death during their hospital stay. The AI system achieved a C-index of 0.80 for predicting individual COVID-19 patients' to critical illness. The AI system successfully stratified the patients into high-risk and low-risk groups with distinct progression risks (p < 0.0001).

CONCLUSIONS : Using CT imaging and clinical data, the AI system successfully predicted time to critical illness for individual patients and identified patients with high risk. AI has the potential to accurately triage patients and facilitate personalized treatment.

KEY POINT : • AI system can predict time to critical illness for patients with COVID-19 by using CT imaging and clinical data.

Wang Robin, Jiao Zhicheng, Yang Li, Choi Ji Whae, Xiong Zeng, Halsey Kasey, Tran Thi My Linh, Pan Ian, Collins Scott A, Feng Xue, Wu Jing, Chang Ken, Shi Lin-Bo, Yang Shuai, Yu Qi-Zhi, Liu Jie, Fu Fei-Xian, Jiang Xiao-Long, Wang Dong-Cui, Zhu Li-Ping, Yi Xiao-Ping, Healey Terrance T, Zeng Qiu-Hua, Liu Tao, Hu Ping-Feng, Huang Raymond Y, Li Yi-Hui, Sebro Ronnie A, Zhang Paul J L, Wang Jianxin, Atalay Michael K, Liao Wei-Hua, Fan Yong, Bai Harrison X


Coronavirus infections, Deep learning, Disease progression, Helical CT