AI-based analysis of CT images for rapid triage of COVID-19 patients

Abstract The COVID-19 pandemic overwhelms the medical resources in the stressed intensive care unit (ICU) capacity and the shortage of mechanical ventilation (MV). We performed CT-based analysis combined with electronic health records and clinical laboratory results on Cohort 1 (n = 1662 from 17 hos...

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Autores principales:	Qinmei Xu, Xianghao Zhan, Zhen Zhou, Yiheng Li, Peiyi Xie, Shu Zhang, Xiuli Li, Yizhou Yu, Changsheng Zhou, Longjiang Zhang, Olivier Gevaert, Guangming Lu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/7652caa276404de8bcb9802ad4e3523c
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spelling	oai:doaj.org-article:7652caa276404de8bcb9802ad4e3523c2021-12-02T17:33:00ZAI-based analysis of CT images for rapid triage of COVID-19 patients10.1038/s41746-021-00446-z2398-6352https://doaj.org/article/7652caa276404de8bcb9802ad4e3523c2021-04-01T00:00:00Zhttps://doi.org/10.1038/s41746-021-00446-zhttps://doaj.org/toc/2398-6352Abstract The COVID-19 pandemic overwhelms the medical resources in the stressed intensive care unit (ICU) capacity and the shortage of mechanical ventilation (MV). We performed CT-based analysis combined with electronic health records and clinical laboratory results on Cohort 1 (n = 1662 from 17 hospitals) with prognostic estimation for the rapid stratification of PCR confirmed COVID-19 patients. These models, validated on Cohort 2 (n = 700) and Cohort 3 (n = 662) constructed from nine external hospitals, achieved satisfying performance for predicting ICU, MV, and death of COVID-19 patients (AUROC 0.916, 0.919, and 0.853), even on events happened two days later after admission (AUROC 0.919, 0.943, and 0.856). Both clinical and image features showed complementary roles in prediction and provided accurate estimates to the time of progression (p < 0.001). Our findings are valuable for optimizing the use of medical resources in the COVID-19 pandemic. The models are available here: https://github.com/terryli710/COVID_19_Rapid_Triage_Risk_Predictor .Qinmei XuXianghao ZhanZhen ZhouYiheng LiPeiyi XieShu ZhangXiuli LiYizhou YuChangsheng ZhouLongjiang ZhangOlivier GevaertGuangming LuNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 4, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Qinmei Xu Xianghao Zhan Zhen Zhou Yiheng Li Peiyi Xie Shu Zhang Xiuli Li Yizhou Yu Changsheng Zhou Longjiang Zhang Olivier Gevaert Guangming Lu AI-based analysis of CT images for rapid triage of COVID-19 patients
description	Abstract The COVID-19 pandemic overwhelms the medical resources in the stressed intensive care unit (ICU) capacity and the shortage of mechanical ventilation (MV). We performed CT-based analysis combined with electronic health records and clinical laboratory results on Cohort 1 (n = 1662 from 17 hospitals) with prognostic estimation for the rapid stratification of PCR confirmed COVID-19 patients. These models, validated on Cohort 2 (n = 700) and Cohort 3 (n = 662) constructed from nine external hospitals, achieved satisfying performance for predicting ICU, MV, and death of COVID-19 patients (AUROC 0.916, 0.919, and 0.853), even on events happened two days later after admission (AUROC 0.919, 0.943, and 0.856). Both clinical and image features showed complementary roles in prediction and provided accurate estimates to the time of progression (p < 0.001). Our findings are valuable for optimizing the use of medical resources in the COVID-19 pandemic. The models are available here: https://github.com/terryli710/COVID_19_Rapid_Triage_Risk_Predictor .
format	article
author	Qinmei Xu Xianghao Zhan Zhen Zhou Yiheng Li Peiyi Xie Shu Zhang Xiuli Li Yizhou Yu Changsheng Zhou Longjiang Zhang Olivier Gevaert Guangming Lu
author_facet	Qinmei Xu Xianghao Zhan Zhen Zhou Yiheng Li Peiyi Xie Shu Zhang Xiuli Li Yizhou Yu Changsheng Zhou Longjiang Zhang Olivier Gevaert Guangming Lu
author_sort	Qinmei Xu
title	AI-based analysis of CT images for rapid triage of COVID-19 patients
title_short	AI-based analysis of CT images for rapid triage of COVID-19 patients
title_full	AI-based analysis of CT images for rapid triage of COVID-19 patients
title_fullStr	AI-based analysis of CT images for rapid triage of COVID-19 patients
title_full_unstemmed	AI-based analysis of CT images for rapid triage of COVID-19 patients
title_sort	ai-based analysis of ct images for rapid triage of covid-19 patients
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/7652caa276404de8bcb9802ad4e3523c
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AI-based analysis of CT images for rapid triage of COVID-19 patients

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