Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets.
Adolescent
Adult
Aged
Aged, 80 and over
Algorithms
Artificial Intelligence
Betacoronavirus
/ isolation & purification
COVID-19
COVID-19 Testing
Child
Child, Preschool
Clinical Laboratory Techniques
/ methods
Coronavirus Infections
/ diagnosis
Deep Learning
Female
Humans
Imaging, Three-Dimensional
/ methods
Lung
/ diagnostic imaging
Male
Middle Aged
Pandemics
Pneumonia, Viral
/ diagnostic imaging
Radiographic Image Interpretation, Computer-Assisted
/ methods
SARS-CoV-2
Tomography, X-Ray Computed
/ methods
Young Adult
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
14 08 2020
14 08 2020
Historique:
received:
08
05
2020
accepted:
13
07
2020
entrez:
16
8
2020
pubmed:
17
8
2020
medline:
26
8
2020
Statut:
epublish
Résumé
Chest CT is emerging as a valuable diagnostic tool for clinical management of COVID-19 associated lung disease. Artificial intelligence (AI) has the potential to aid in rapid evaluation of CT scans for differentiation of COVID-19 findings from other clinical entities. Here we show that a series of deep learning algorithms, trained in a diverse multinational cohort of 1280 patients to localize parietal pleura/lung parenchyma followed by classification of COVID-19 pneumonia, can achieve up to 90.8% accuracy, with 84% sensitivity and 93% specificity, as evaluated in an independent test set (not included in training and validation) of 1337 patients. Normal controls included chest CTs from oncology, emergency, and pneumonia-related indications. The false positive rate in 140 patients with laboratory confirmed other (non COVID-19) pneumonias was 10%. AI-based algorithms can readily identify CT scans with COVID-19 associated pneumonia, as well as distinguish non-COVID related pneumonias with high specificity in diverse patient populations.
Identifiants
pubmed: 32796848
doi: 10.1038/s41467-020-17971-2
pii: 10.1038/s41467-020-17971-2
pmc: PMC7429815
doi:
Types de publication
Journal Article
Research Support, N.I.H., Intramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
4080Subventions
Organisme : NCI NIH HHS
ID : 75N91019D00024
Pays : United States
Organisme : NCI NIH HHS
ID : 75N91019F00129
Pays : United States
Organisme : Intramural NIH HHS
ID : ZIA CL040015
Pays : United States
Organisme : Intramural NIH HHS
ID : ZID BC011242
Pays : United States
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