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The value of longitudinal clinical data and paired CT scans in predicting the deterioration of COVID-19 revealed by an artificial intelligence system

Authors :
Xiaoyang Han
Ziqi Yu
Yaoyao Zhuo
Botao Zhao
Yan Ren
Lorenz Lamm
Xiangyang Xue
Jianfeng Feng
Carsten Marr
Fei Shan
Tingying Peng
Xiao-Yong Zhang
Source :
iScience, Vol 25, Iss 5, Pp 104227- (2022)
Publication Year :
2022
Publisher :
Elsevier, 2022.

Abstract

Summary: The respective value of clinical data and CT examinations in predicting COVID-19 progression is unclear, because the CT scans and clinical data previously used are not synchronized in time. To address this issue, we collected 119 COVID-19 patients with 341 longitudinal CT scans and paired clinical data, and we developed an AI system for the prediction of COVID-19 deterioration. By combining features extracted from CT and clinical data with our system, we can predict whether a patient will develop severe symptoms during hospitalization. Complementary to clinical data, CT examinations show significant add-on values for the prediction of COVID-19 progression in the early stage of COVID-19, especially in the 6th to 8th day after the symptom onset, indicating that this is the ideal time window for the introduction of CT examinations. We release our AI system to provide clinicians with additional assistance to optimize CT usage in the clinical workflow.

Details

Language :
English
ISSN :
25890042
Volume :
25
Issue :
5
Database :
Directory of Open Access Journals
Journal :
iScience
Publication Type :
Academic Journal
Accession number :
edsdoj.32b0606fcb2453ab4ce51923534ca05
Document Type :
article
Full Text :
https://doi.org/10.1016/j.isci.2022.104227