About: Serial Quantitative Chest CT Assessment of COVID-19: Deep-Learning Approach

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Serial Quantitative Chest CT Assessment of COVID-19: Deep-Learning Approach
Creator	Ai, Tao Tao, Qian Xia, Liming Huang, Lu Kang, Han Han, Rui Yu, Pengxin
source	PMC
abstract	PURPOSE: To quantitatively evaluate lung burden changes in patients with COVID-19 using serial CT scan by an automated deep learning method. MATERIALS AND METHODS: Patients with COVID-19 who underwent chest CT between 1(st) January 2020 and 3(rd) February 2020 were retrospectively evaluated. Patients were divided into mild, moderate, severe, and critical types, according to their baseline clinical, laboratory, and CT findings. CT lung opacification percentage of the whole lung and five lobes were automatically quantified by a commercial deep learning software, and compared over follow-ups CT scans. Longitudinal changes of the CT quantitative parameter were also compared among the four clinical types. RESULTS: A total of 126 patients with COVID-19 (age 52 years ± 15 years, 53.2% males) were evaluated, including 6 mild, 94 moderate, 20 severe and 6 critical cases. CT-derived opacification percentage was significantly different among clinical groups at baseline, gradually progressing from mild to critical type (all P < 0.01). Overall, the whole-lung opacification percentage significantly increased between baseline CT and 1(st) follow-up CT (median [interquartile range]; 3.6% [0.5%,12.1%] vs 8.7% [2.7%,21.2%], P < 0.01). No significant progression of the opacification percentages was noted between the 1(st) follow-up and 2(nd) follow-up CT (8.7% [2.7%,21.2%] vs 6.0% [1.9%,24.3%], P=0.655). CONCLUSION: The quantification of lung opacification in COVID-19 measured on chest CT by a commercially available deep-learning-based tool was significantly different among different clinical severity groups. This approach could potentially eliminate the subjectivity in the initial assessment and follow up of pulmonary findings in COVID-19.
has issue date	2020-03-30(xsd:dateTime)
bibo:doi	10.1148/ryct.2020200075
has license	no-cc
sha1sum (hex)	18355614b3e03a911575a3b58bf0fbf99f32fcaf
schema:url	https://doi.org/10.1148/ryct.2020200075
resource representing a document's title	Serial Quantitative Chest CT Assessment of COVID-19: Deep-Learning Approach
has PubMed Central identifier	PMC7233442
schema:publication	Radiol Cardiothorac Imaging
resource representing a document's body	covid:18355614b3e03a911575a3b58bf0fbf99f32fcaf#body_text
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