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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.
European radiology, 01/2022, Letnik: 32, Številka: 1Journal Article
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.
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Dostop do baze podatkov JCR je dovoljen samo uporabnikom iz Slovenije. Vaš trenutni IP-naslov ni na seznamu dovoljenih za dostop, zato je potrebna avtentikacija z ustreznim računom AAI.
Leto | Faktor vpliva | Izdaja | Kategorija | Razvrstitev | ||||
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JCR | SNIP | JCR | SNIP | JCR | SNIP | JCR | SNIP |
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Povezave do osebnih bibliografij avtorjev | Povezave do podatkov o raziskovalcih v sistemu SICRIS |
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Vir: Osebne bibliografije
in: SICRIS
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