Artificial intelligence for prediction of COVID-19 progression using CT imaging and clinical data

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...

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Dettagli Bibliografici
Pubblicato in:Eur Radiol
Autori principali: 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.
Natura: Artigo
Lingua:Inglês
Pubblicazione: Springer Berlin Heidelberg 2021
Soggetti:
Chest
Accesso online:https://ncbi.nlm.nih.gov/pmc/articles/PMC8256200/
https://ncbi.nlm.nih.gov/pubmed/34223954
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1007/s00330-021-08049-8
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