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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)
Verlagsversion Forschungsdaten DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
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.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Artificial Intelligence ; Health Sciences ; Machine Learning ; Microbiology
ISSN (print) / ISBN 2589-0042
e-ISSN 2589-0042
Zeitschrift iScience
Quellenangaben Band: 25, Heft: 5, Seiten: , Artikelnummer: 104227 Supplement: ,
Verlag Elsevier
Verlagsort Amsterdam ; Bosten ; London ; New York ; Oxford ; Paris ; Philadelphia ; San Diego ; St. Louis
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Helmholtz Artifical Intelligence Cooperation Unit (HAICU)
Helmholtz Pioneer Campus (HPC)
Institute of AI for Health (AIH)
Förderungen 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