Artificial intelligence-assisted quantification of COVID-19 pneumonia burden from computed tomography improves prediction of adverse outcomes over visual scoring systems

We aimed to evaluate the effectiveness of utilizing artificial intelligence (AI) to quantify the extent of pneumonia from chest CT scans, and to determine its ability to predict clinical deterioration or mortality in patients admitted to the hospital with COVID-19 in comparison to semi-quantitative...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	British journal of radiology 2023-09, Vol.96 (1149), p.20220180
Hauptverfasser:	Grodecki, Kajetan, Killekar, Aditya, Simon, Judit, Lin, Andrew, Cadet, Sebastien, McElhinney, Priscilla, Chan, Cato, Williams, Michelle C, Pressman, Barry D, Julien, Peter, Li, Debiao, Chen, Peter, Gaibazzi, Nicola, Thakur, Udit, Mancini, Elisabetta, Agalbato, Cecilia, Munechika, Jiro, Matsumoto, Hidenari, Menè, Roberto, Parati, Gianfranco, Cernigliaro, Franco, Nerlekar, Nitesh, Torlasco, Camilla, Pontone, Gianluca, Maurovich-Horvat, Pal, Slomka, Piotr J, Dey, Damini
Format:	Artikel
Sprache:	eng
Schlagworte:	Aged Aged, 80 and over Artificial Intelligence Clinical Deterioration COVID-19 - diagnostic imaging Female Hospital Mortality Humans Lung Male Middle Aged Pneumonia - diagnostic imaging Retrospective Studies SARS-CoV-2 Tomography, X-Ray Computed - methods
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!