Open AccessAI-Based Quantitative CT Analysis of Temporal Changes According to Disease Severity in COVID-19 Pneumonia
Author(s) -
Selin Ardalı Düzgün,
Gamze Durhan,
Figen Başaran Demirkazık,
İlim Irmak,
Jale Karakaya,
Erhan Akpınar,
Meltem Gülsün Akpınar,
Ahmet Çağkan İnkaya,
Serpil Öcal,
Arzu Topeli,
Orhan Macit Arıyürek
Publication year - 2021
Publication title -
journal of computer assisted tomography
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.53
H-Index - 93
eISSN - 1532-3145
pISSN - 0363-8715
DOI - 10.1097/rct.0000000000001224
Subject(s) - medicine , ground glass opacity , opacity , pneumonia , covid-19 , lung , nuclear medicine , gastroenterology , radiology , disease , adenocarcinoma , physics , cancer , infectious disease (medical specialty) , optics
To quantitatively evaluate computed tomography (CT) parameters of coronavirus disease 2019 (COVID-19) pneumonia an artificial intelligence (AI)-based software in different clinical severity groups during the disease course.