Han, X.* ; Yu, Z.* ; Zhuo, Y.* ; Zhao, B.* ; Ren, Y.* ; Lamm, L. ; Xue, X.* ; Feng, J.* ; Marr, C. ; Shan, F.* ; Peng, T. ; Zhang, X.Y.*
The value of longitudinal clinical data and paired CT scans in predicting the deterioration of COVID-19 revealed by an artificial intelligence system.
iScience 25:104227 (2022)
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 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.
Impact Factor
Scopus SNIP
Web of Science
Times Cited
Scopus
Cited By
Altmetric
Publication type
Article: Journal article
Document type
Scientific Article
Thesis type
Editors
Keywords
Artificial Intelligence ; Health Sciences ; Machine Learning ; Microbiology
Keywords plus
Language
english
Publication Year
2022
Prepublished in Year
HGF-reported in Year
2022
ISSN (print) / ISBN
2589-0042
e-ISSN
2589-0042
ISBN
Book Volume Title
Conference Title
Conference Date
Conference Location
Proceedings Title
Quellenangaben
Volume: 25,
Issue: 5,
Pages: ,
Article Number: 104227
Supplement: ,
Series
Publisher
Elsevier
Publishing Place
Amsterdam ; Bosten ; London ; New York ; Oxford ; Paris ; Philadelphia ; San Diego ; St. Louis
Day of Oral Examination
0000-00-00
Advisor
Referee
Examiner
Topic
University
University place
Faculty
Publication date
0000-00-00
Application date
0000-00-00
Patent owner
Further owners
Application country
Patent priority
Reviewing status
Peer reviewed
POF-Topic(s)
30205 - Bioengineering and Digital Health
30203 - Molecular Targets and Therapies
Research field(s)
Enabling and Novel Technologies
Pioneer Campus
PSP Element(s)
G-530006-001
G-510008-001
G-540007-001
Grants
Horizon 2020
Science and Technology Commission of Shanghai Municipality
Fudan University
National Natural Science Foundation of China
European Research Council
Horizon 2020 Framework Programme
ZJLab
Copyright
Erfassungsdatum
2022-07-28