文章基本信息

标题：The value of longitudinal clinical data and paired CT scans in predicting the deterioration of COVID-19 revealed by an artificial intelligence system
本地全文：下载
作者：Xiaoyang Han ; Ziqi Yu ; Yaoyao Zhuo 等
期刊名称：iScience
印刷版ISSN：2589-0042
出版年度：2022
卷号：25
期号：5
页码：1-16
DOI：10.1016/j.isci.2022.104227
语种：English
出版社：Elsevier
摘要：SummaryThe 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 6thto 8thday 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.Graphical abstractDisplay OmittedHighlights•COVID-19 patients with 341 longitudinal CT scans and paired clinical data included•A new AI model for the prediction of COVID-19 progression was developed•CT scans show significant add-on value over clinical data for the prediction•Day 6–8 after the onset of COVID-19 symptoms is an ideal time window for a CT scanHealth sciences; Microbiology; Artificial intelligence; Machine learning