| Property | Value |
|---|
|
?:abstract
|
-
Computed tomography (CT) is the preferred imaging method for diagnosing 2019 novel coronavirus (COVID19) pneumonia. We aimed to construct a system based on deep learning for detecting COVID-19 pneumonia on high resolution CT. For model development and validation, 46,096 anonymous images from 106 admitted patients, including 51 patients of laboratory confirmed COVID-19 pneumonia and 55 control patients of other diseases in Renmin Hospital of Wuhan University were retrospectively collected. Twenty-seven prospective consecutive patients in Renmin Hospital of Wuhan University were collected to evaluate the efficiency of radiologists against 2019-CoV pneumonia with that of the model. An external test was conducted in Qianjiang Central Hospital to estimate the system’s robustness. The model achieved a per-patient accuracy of 95.24% and a per-image accuracy of 98.85% in internal retrospective dataset. For 27 internal prospective patients, the system achieved a comparable performance to that of expert radiologist. In external dataset, it achieved an accuracy of 96%. With the assistance of the model, the reading time of radiologists was greatly decreased by 65%. The deep learning model showed a comparable performance with expert radiologist, and greatly improved the efficiency of radiologists in clinical practice.
|
|
is
?:annotates
of
|
|
|
?:creator
|
|
|
?:doi
|
-
10.1038/s41598-020-76282-0
|
|
?:doi
|
|
|
?:journal
|
|
|
?:license
|
|
|
?:pdf_json_files
|
-
document_parses/pdf_json/c30ba3402ab304fa161d20989f43d54b5de4e98c.json
|
|
?:pmc_json_files
|
-
document_parses/pmc_json/PMC7645624.xml.json
|
|
?:pmcid
|
|
|
?:pmid
|
|
|
?:pmid
|
|
|
?:publication_isRelatedTo_Disease
|
|
|
?:sha_id
|
|
|
?:source
|
|
|
?:title
|
-
Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography
|
|
?:type
|
|
|
?:year
|
|
![[This page as RDF]](https://research.tib.eu/covid-19/static/rdf-icon.gif){kind=icon}