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

Authors :
Wang R
Jiao Z
Yang L
Choi JW
Xiong Z
Halsey K
Tran TML
Pan I
Collins SA
Feng X
Wu J
Chang K
Shi LB
Yang S
Yu QZ
Liu J
Fu FX
Jiang XL
Wang DC
Zhu LP
Yi XP
Healey TT
Zeng QH
Liu T
Hu PF
Huang RY
Li YH
Sebro RA
Zhang PJL
Wang J
Atalay MK
Liao WH
Fan Y
Bai HX
Source :
European radiology [Eur Radiol] 2022 Jan; Vol. 32 (1), pp. 205-212. Date of Electronic Publication: 2021 Jul 05.
Publication Year :
2022

Abstract

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.<br />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.<br />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).<br />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.<br />Key Point: • AI system can predict time to critical illness for patients with COVID-19 by using CT imaging and clinical data.<br /> (© 2021. European Society of Radiology.)

Details

Language :
English
ISSN :
1432-1084
Volume :
32
Issue :
1
Database :
MEDLINE
Journal :
European radiology
Publication Type :
Academic Journal
Accession number :
34223954
Full Text :
https://doi.org/10.1007/s00330-021-08049-8