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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Bibliographic Details
Main Authors: Qinmei Xu, Xianghao Zhan, Zhen Zhou, Yiheng Li, Peiyi Xie, Shu Zhang, Xiuli Li, Yizhou Yu, Changsheng Zhou, Longjiang Zhang, Olivier Gevaert, Guangming Lu
Format: Article
Language:English
Published: Nature Publishing Group 2021-04-01
Series:npj Digital Medicine
Online Access:https://doi.org/10.1038/s41746-021-00446-z
Description
Summary: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 .
ISSN:2398-6352