Prediction of disease progression in patients with COVID-19 by artificial intelligence assisted lesion quantification.
Adult
Aged
Aged, 80 and over
Area Under Curve
Artificial Intelligence
COVID-19
/ diagnostic imaging
Disease Progression
Female
Humans
Image Processing, Computer-Assisted
Lung
/ diagnostic imaging
Male
Middle Aged
Multivariate Analysis
ROC Curve
Retrospective Studies
SARS-CoV-2
/ isolation & purification
Severity of Illness Index
Tomography, X-Ray Computed
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
16 12 2020
16 12 2020
Historique:
received:
04
06
2020
accepted:
27
11
2020
entrez:
17
12
2020
pubmed:
18
12
2020
medline:
29
12
2020
Statut:
epublish
Résumé
To investigate the value of artificial intelligence (AI) assisted quantification on initial chest CT for prediction of disease progression and clinical outcome in patients with coronavirus disease 2019 (COVID-19). Patients with confirmed COVID-19 infection and initially of non-severe type were retrospectively included. The initial CT scan on admission was used for imaging analysis. The presence of ground glass opacity (GGO), consolidation and other findings were visually evaluated. CT severity score was calculated according to the extent of lesion involvement. In addition, AI based quantification of GGO and consolidation volume were also performed. 123 patients (mean age: 64.43 ± 14.02; 62 males) were included. GGO + consolidation was more frequently revealed in progress-to-severe group whereas pure GGO was more likely to be found in non-severe group. Compared to non-severe group, patients in progress-to-severe group had larger GGO volume (167.33 ± 167.88 cm
Identifiants
pubmed: 33328512
doi: 10.1038/s41598-020-79097-1
pii: 10.1038/s41598-020-79097-1
pmc: PMC7745019
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
22083Subventions
Organisme : Medicine and Engineering Interdisciplinary Research Fund of Shanghai Jiao Tong University
ID : YG2020YQ18
Pays : International
Organisme : Medicine and Engineering Interdisciplinary Research Fund of Shanghai Jiao Tong University
ID : YG2020YQ18
Pays : International
Références
Am J Clin Pathol. 2020 May 5;153(6):725-733
pubmed: 32275742
Comput Biol Med. 2020 Sep;124:103949
pubmed: 32798922
N Engl J Med. 2020 Mar 26;382(13):1199-1207
pubmed: 31995857
N Engl J Med. 2020 Apr 30;382(18):1708-1720
pubmed: 32109013
JAMA. 2020 May 26;323(20):2052-2059
pubmed: 32320003
Invest Radiol. 2020 Jun;55(6):327-331
pubmed: 32118615
AJR Am J Roentgenol. 2000 Nov;175(5):1329-34
pubmed: 11044035
Lancet Infect Dis. 2020 Apr;20(4):425-434
pubmed: 32105637
Eur Radiol. 2020 Aug 1;:
pubmed: 32740817
Radiology. 2020 Sep;296(3):E156-E165
pubmed: 32339081
Radiographics. 2018 May-Jun;38(3):719-739
pubmed: 29757717
Theranostics. 2020 Apr 27;10(12):5613-5622
pubmed: 32373235
Radiology. 1982 Apr;143(1):29-36
pubmed: 7063747
Radiology. 2004 Mar;230(3):836-44
pubmed: 14990845
AJR Am J Roentgenol. 2020 May;214(5):1065-1071
pubmed: 32130041
Eur J Radiol. 2020 Oct;131:109256
pubmed: 32919265
Radiology. 2020 Aug;296(2):E15-E25
pubmed: 32083985
Radiology. 2008 Mar;246(3):697-722
pubmed: 18195376
Invest Radiol. 2020 Jul;55(7):412-421
pubmed: 32304402
Radiology. 2020 Aug;296(2):E32-E40
pubmed: 32101510
Eur Radiol. 2019 Sep;29(9):4742-4750
pubmed: 30778717
Lung Cancer. 2018 Jan;115:34-41
pubmed: 29290259